[orcid=0000-0002-3913-0369]

1]organization=School of Electronic Engineering and Computer Science, Queen
Mary University of London, London, UK

2]organization=Shenzhen Institute of Advanced Technology, Chinese Academy of
Sciences, Shenzhen, China

3]organization=Department of Computer Science and York Centre for Quantum
Technologies, University of York, York, UK

4]organization=Department of Software Engineering, University of Waikato,
Hamilton, Aotearoa New Zealand

5]organization=School of Computer Science, University of Birmingham,
Birmingham, UK

6]organization=Department of Information Systems and Business Analytics, RMIT
University, Melbourne, Australia

7]organization=Faculty of Computer and Information Science, University of
Ljubljana, Ljubljana, Slovenia

8]organization=Center for Applied Mathematics, Tianjin University, Tianjin,
China

9]organization=Machine Intelligence Research Labs, Auburn, WA, USA

10]organization=Center for Artificial Intelligence, Innopolis University,
Innopolis, Russia

11]organization=Jackson School of Geosciences, University of Texas at Austin,
Austin, Texas, USA

12]organization=Centre for Climate Change Research, Indian Institute of
Tropical Meteorology, Pune, India

13]organization=Walker Department of Mechanical Engineering, Cockrell School
of Engineering, The University of Texas at Austin, Texas, USA

14]organization=Dell Technologies, Austin, Texas, USA

15]organization=Department of Computer Science and Engineering, Indian
Institute of Technology, Kharagpur, India

16]organization=Department of Computer Science, University of Sharjah,
Sharjah, UAE

17]organization=Institute for Manufacturing, Department of Engineering,
University of Cambridge, Cambridge, UK

18]organization=Department of Computer Science, University of Western Ontario,
London, Canada

19]organization=School of Computer Science and Engineering, The University of
New South Wales (UNSW), Sydney, Australia

20]organization=Department of Computer Science, University of Manchester,
Oxford Road, Manchester, UK

21]organization=Distributed Systems Group, Vienna University of Technology,
Vienna, Austria

22]organization=School of Computer Science and Informatics, Cardiff
University, Cardiff, UK

23]organization=Faculty of Informatics, Vienna University of Technology,
Vienna, Austria

[cor1]Corresponding author at: School of Electronic Engineering and Computer
Science, Queen Mary University of London, London, E1 4NS, UK.

# AI for Next Generation Computing: Emerging Trends and Future Directions

Sukhpal Singh Gill<EMAIL_ADDRESS>[  Minxian Xu<EMAIL_ADDRESS>[  Carlo
Ottaviani<EMAIL_ADDRESS>[  Panos Patros
<EMAIL_ADDRESS>[  Rami Bahsoon<EMAIL_ADDRESS>[  Arash
Shaghaghi<EMAIL_ADDRESS>[  Muhammed Golec<EMAIL_ADDRESS>Vlado Stankovski<EMAIL_ADDRESS>[  Huaming Wu
<EMAIL_ADDRESS>[  Ajith Abraham<EMAIL_ADDRESS>[  [  Manmeet Singh
<EMAIL_ADDRESS>[  [  Harshit Mehta<EMAIL_ADDRESS>[  [
Soumya K. Ghosh<EMAIL_ADDRESS>[  Thar Baker<EMAIL_ADDRESS>[
Ajith Kumar Parlikad<EMAIL_ADDRESS>[  Hanan Lutfiyya<EMAIL_ADDRESS>[
Salil S. Kanhere<EMAIL_ADDRESS>[  Rizos Sakellariou
<EMAIL_ADDRESS>[  Schahram Dustdar<EMAIL_ADDRESS>[  Omer
Rana<EMAIL_ADDRESS>[  Ivona Brandic<EMAIL_ADDRESS>[  Steve
Uhlig<EMAIL_ADDRESS>
###### Abstract

Autonomic computing investigates how systems can achieve (user) specified
“control” outcomes on their own, without the intervention of a human operator.
Autonomic computing fundamentals have been substantially influenced by those
of control theory for closed and open-loop systems. In practice, complex
systems may exhibit a number of concurrent and inter-dependent control loops.
Despite research into autonomic models for managing computer resources,
ranging from individual resources (e.g., web servers) to a resource ensemble
(e.g., multiple resources within a data center), research into integrating
Artificial Intelligence (AI) and Machine Learning (ML) to improve resource
autonomy and performance at scale continues to be a fundamental challenge. The
integration of AI/ML to achieve such autonomic and self-management of systems
can be achieved at different levels of granularity, from full to human-in-the-
loop automation. In this article, leading academics, researchers,
practitioners, engineers, and scientists in the fields of cloud computing,
AI/ML, and quantum computing join to discuss current research and potential
future directions for these fields. Further, we discuss challenges and
opportunities for leveraging AI and ML in next generation computing for
emerging computing paradigms, including cloud, fog, edge, serverless and
quantum computing environments.

###### keywords:

Next Generation Computing Artificial Intelligence Cloud Computing Fog
Computing Edge Computing Serverless Computing Quantum Computing Machine
Learning

##  1 Introduction

Autonomic Computing Initiative (ACI) from IBM were among the first industry-
wide initiatives for the design of computer systems that require limited human
interaction to achieve performance targets [1]. The Tivoli systems division at
IBM focused initially at performance tuning of the DB2 database system using
autonomic computing principles. The initiative was heavily inspired by
observations from the functioning and coordination of the human nervous system
and human cognition—i.e., the autonomic nervous system acts and reacts to
stimuli independent of an individual’s conscious input; an autonomic computing
environment functions with a high level of Artificial Intelligence (AI), while
remaining invisible to users [2]. Additionally, a human nervous system
achieves multiple outcomes concurrently and seamlessly (e.g., internal
temperature changes, breathing rates fluctuate, and glands secrete hormones as
a response to stimulus) adhering to pre-defined/evolved “limits” and norms,
and acting on impulses sensed or learned from the body itself or the
environment. As for the human body, an autonomic computing environment is
expected to work in response to the data it collects, sensed or learned,
without an individual directly controlling functions used to manage a system
[3].

Autonomic computing—also referred to as self-adaptive systems—is a field of
investigation that studies how systems can achieve desirable behaviours on
their own [4]. It is common for these systems to be referred to as “self-*”
systems, where “*” stands for the behaviour type [5], such as: self-
configuration, self-optimization, self-protection and self-healing [6].

An autonomic system’s capacity to adapt to environmental changes is referred
to as “self-configuring” [7]. The system automatically upgrades missing or
obsolete components depending on error messages/alerts generated by a
monitoring system [8]. A self-optimizing autonomic system is one that can
enhance its own performance by successfully completing computational jobs
submitted to it, reducing resource overload and under-utilization [9]. Self-
protection is an autonomic system’s capacity to defend itself against
potential cyber-attacks and intrusions. The system should also be detecting
and preventing harmful assaults on the autonomic coordinator managing the
overall system [10]. Self-healing is a system’s ability to discover, evaluate
and recover from errors on its own, without the need for human intervention
[2]. By decreasing or eliminating the effect of errors on execution, this
self-* property improves performance through fault tolerance [11].

The ultimate vision is that neither self-managed systems nor self-healing
systems need to be configured or updated manually [12]. In a broader sense,
self-managed systems should be capable of controlling all of the
aforementioned behaviours [13].

Different practical systems realise these outcomes to varying levels of
granularity and success. Also, the level of human intervention and control can
vary. As part of IBM’s Autonomic Computing paradigm, the Autonomic Manager
(AM) is a smart entity that interacts with the environment via management
interfaces (Sensors and Effectors) and performs actions based on the
information received from sensors and rules established in a low-level
knowledge base. The AM is set up by an administrator using high-level warnings
and acts. Figure 1 illustrates IBM’s autonomic approach in operation [1].
Initial monitors acquire sensor data for regular inspection of Quality of
Service (QoS) metrics whilst engaging with external hardware and send this
data to the next component for further evaluation. In the Analyze and Plan
modules, data collected from the monitoring module is analysed and appropriate
action plans are drawn up in response to system warnings. Using the results of
the data analysis, this autonomic system takes appropriate actions in response
to the generated warnings. After a thorough review, which includes
verification and validation to provide guarantees that the adaptation will
indeed work, the plan is put into action by the Executor, whose primary goal
is to ensure that the QoS of an executing application is maintained. An
Executor monitors changes in the knowledge base and acts based on the results
of the analysis.

###  1.1 AI/ML for Next Generation Computing: A Vision

AI and ML can be used to support and develop autonomic behaviours based on
data collected about systems operations. ML techniques, for example, can be
used to discover patterns in the workload, where these patterns can be used to
optimise resource management [14]. Additionally, to mitigate model
uncertainty, ML-based dynamical system identification methods, such as
recurrent neural networks, could be adaptively invoked by the autonomic
manager to achieve self-learning. Thus, black- and gray-box models of the
managed system can be generated during a concept drift and subsequently
verified to check their sanity or even, detect mission-critical alterations of
the system’s operation [15]. Further, AI may be employed in the analysis and
planning stages of autonomic systems that are often arranged as monitor-
analyze-plan and execute (MAPE) cycles [16], in addition to the use of
techniques from control theory. It is the combination of feedback control with
data-driven model construction using ML that offers key benefits in support
autonomic self-management.

Figure 1: MAPE-K loop for Autonomic Computing

Among the notable types of autonomous computing solutions: feedback-based
control is one common solution. The use of self-organizing systems, such as
particle swarm optimisation, cellular automata and genetic algorithms, are
others. In the first category of solutions, systematic techniques for
designing closed-loop systems capable of tracking system performance and
altering control parameters are provided by autonomic computing [17]. There is
a vast corpus of control theory literature and design tools that are used in
these techniques. When it comes to the second type of solution, a variety of
newly developing peer-to-peer approaches are now being employed to create
massively scaled self-managing networks [18].

###  1.2 Motivation and Aim

Autonomic computing has been integrated in computing paradigms such as cloud,
fog, edge, serverless and quantum computing using AI/ML techniques [19]. The
use of autonomic computing techniques is particularly significant when there
is a large number of potential configuration options for a system. The greater
the potential parameter space over which configuration options can vary, the
greater the potential to optimise search over this space of possible options.
Autonomic computing techniques are most useful under the hood, i.e. as a
programmatic interface that can be invoked directly [20] from an application.

There are many applications that can manage node failures, network
setup/updates and a limited ability to carry out performance optimization on
their own since most peer-to-peer networks are fundamentally autonomous. AI-
and ML-based self-managing capabilities are becoming increasingly common in
web services and data center management software, allowing these systems to
automatically adapt to shifting workloads [21]. However, autonomic features
are not always included in schedulers and workflow managers, as such systems
frequently lack the ability to monitor system condition and provide real-time
feedback, making it difficult for these systems to be fully autonomous [22].
Integrating “tuning” capability that makes use of AI/ML techniques can extend
the capability of such systems. For instance, self-managed computing
platforms, such as Hadoop/MapReduce, provide self-healing and self-organizing
capabilities that enable the use of a large number of resources [23].

AI- and ML-based autonomic computing will become prevalent with increasing
scale and interconnectivity of our systems, making manual administration and
adaptation of such systems challenging and expensive. We expect AI- and ML-
based autonomic computing will be the norm in the future—with human users
still able to influence the behaviour of these systems through the use of
judiciously integrated interfaces. Crucially, with the advent of cyberphysical
systems and digital twins, quality-assured and mission-critical adaptations
will become mandatory because the self-adaptive software will be responsible
for physical assets, such as the unit operations of a processing plant.

But how should self-adaptive systems and AI/ML be combined? According to IBM,
an autonomic system must meet the following eight criteria for computing
systems using AI and ML techniques [24, 25, 26, 2, 8, 9, 10, 27]:

  * •

The resources that are available to the AI-powered system, as well as the
capabilities and limits of the system, must be known by the system.

  * •

As the computing environment changes, e.g., because of a concept drift, the
system must be able to adapt and reconfigure autonomously.

  * •

An efficient computer process requires a system that can maximise its
performance via AI- and ML-based prediction.

  * •

When an error occurs, the system should be able to fix itself or redirect
processes away from the source of the issue.

  * •

To ensure overall system security and integrity, the system must be able to
detect, identify, and respond to numerous forms of threats automatically.

  * •

As the environment changes, the system must be able to interact with and
develop communication protocols with other systems.

Despite the system’s transparency, it must be able to predict demand on its
resources, which can be forecasted with AI/ML techniques. Small, even
inconspicuous computers will be able to communicate with each other across
more linked networks, leading to the notion of “The Internet of Everything
(IoE)”, thanks in part to the emergence of ubiquitous computing and autonomic
computing [28]. Crucially, AI-powered self-adaptive systems promise to cost-
effectively and sustainably meet changing requirements in a changing
environment and in the presence of uncertainty—vs., just adding more and more
resources. Hence, in conjunction with the latest AI and ML techniques,
autonomic computing is being studied and applied by a number of industry
giants.

###  1.3 Benefits of AI/ML-integrated Next Generation Computing

AI-based Autonomic computing’s primary advantage is lower total cost of
ownership [29]. As a result, maintenance expenditures will be significantly
reduced. There will also be a reduction in the number of people needed to
maintain the systems. AI-powered automated IT systems will save deployment and
maintenance costs, time, and boost IT system stability. Companies will be able
to better manage their business using IT systems that can adopt and implement
directives based on business strategy and can make alterations in response to
changing surroundings, according to the higher-order advantages. Server
consolidation is another benefit of using AI-based autonomic computing, since
it reduces the cost and human labour required to maintain huge server farms
[30]. Management of computer systems should be made easier using AI for
autonomous computing. As a result, computing systems will be significantly
improved. Another example of an application is server load distribution, which
may be accomplished by distributing work across several servers [31]. Further,
cost-effective and sustainable power supply policies can be accomplished by
continuously monitoring the power supply.

As a consequence of AI, the following changes have occurred in autonomic
computing:

  * •

Cost-effective: Using computer systems instead of on-site data centres has its
advantages. Despite the high initial costs, organisations may easily acquire
AI technology via a monthly charge in the cloud. Systems using AI may analyse
data without involving a human being.

  * •

Autonomic: Enterprises may become more efficient, strategic, and insight-
driven through the use of AI cloud computing. AI has the potential to boost
productivity by automating tedious and repetitive tasks, as well as doing data
analysis without the use of operator interaction.

  * •

Data Organization: Real-time personalisation, anomaly detection, and
management scenario prediction may be achieved by integrating AI technology
with Google Cloud Stream analytics.

  * •

Making Intelligent Decisions: Intelligence-based data security is critical as
more cloud-based apps are deployed. Network traffic tracing and analysis made
possible by AI-powered network security technologies. As soon as an
abnormality is discovered, AI-powered systems can raise a red signal. Such
strategy safeguards crucial information.

###  1.4 Related Surveys and Our Contributions

As the area of computing continues to expand, there is a need for a fresh
visionary work to review, upgrade and consolidate the current evidence and
discuss potential trends and future perspectives in the field of computing.
Varghese and Buyya [32] introduced an innovative survey on next generation
cloud computing, which does not consider AI/ML. Abdulkareem et al. [33]
presented a review on AI for fog computing only. Massimo et al. [34] explored
literature for AI-based edge computing. Gill et al. [19] presented a review on
AI for cloud computing. The surveys from Kumar el al. [35] and Li et al. [36]
highlighted the potential role of AI in quantum computing. The suitability of
AI for serverless computing is described in Hassan et al. [37].

By combining AI/ML with cloud, fog, edge, serverless, and quantum computing,
we’ve created the first review of its kind. Adding to the previous surveys,
this new research gives a new imaginative approach to assessing and
identifying the most current research challenges. Table 1 compares our review
with existing surveys based on different criteria.

Table 1: Comparison of Our Survey with Other Survey Articles. $\times$:=
method supports the property.

Works | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Publication Year
---|---|---|---|---|---|---|---|---|---|---|---|---
Varghese and Buyya [32] |  |  | x |  |  |  |  |  |  |  |  | 2018
Abdulkareem et al. [33] |  | x |  | x |  |  |  |  |  |  |  | 2019
Gill et al. [19] |  | x | x |  |  |  |  |  |  |  |  | 2019
Massimo et al. [34] |  | x |  |  | x |  |  |  |  |  |  | 2020
Li et al. [36] |  | x |  |  |  |  | x |  |  |  |  | 2020
Kumar el al. [35] |  | x |  |  |  |  | x |  |  |  |  | 2021
Hassan et al. [37] |  | x |  |  |  | x |  |  |  |  |  | 2021
Our Survey (This Paper) | x | x | x | x | x | x | x | x | x | x | x | 2022

Abbreviations: 1: Prospective Model, 2: AI, 3: Cloud Computing, 4: Fog
Computing, 5: Edge Computing, 6: Serverless Computing, 7: Quantum Computing,
8: Explainable AI (XAI), 9: Risks and Benefits of AI-integrated Next
Generation Computing, 10: Hype Cycle, and 11: Intelligent Edge.

####  1.4.1 Our Focus

This paper leverages the expanding domain of Internet of Things (IoT), edge
computing and the computing continuum as an exemplar application for AI-
powered adaptation. There is a tremendous growth on applications that leverage
such technologies, such as smart agriculture, environmental monitoring,
industrial digital twins, smart cities, management of renewable energy
generation/storage, etc. Nevertheless, our discussion can be expanded to other
fields as well.

###  1.5 Article Organization

The rest of this article is organized as illustrated in Figure 2. Section 2
proposes a conceptual model. Section 3 is presenting vision and discussing
various emerging trends in AI for cloud, fog, edge, serverless and quantum
computing. Section 4 discusses the new research developments related to
autonomic computing with embedded intelligence. Section 5 discusses the use of
Explainable AI (XAI) for next-generation computing. Section 6 presents the
potential risks of autonomic computing approaches that make use of AI/ML
algorithms. Section 7 gives the hype cycle for autonomic computing and
highlights the future directions. Section 8 concludes and summarizes the
paper.

Figure 2: The organization of this survey

##  2 A Prospective Model for Next Generation Computing Systems

To show the relationship between AI/ML and autonomous computing systems, we
propose a prospective software architecture model as shown in Figure 3. Our
proposal integrates advanced technologies to offer effective computing
services that fulfill the demand for a variety of IoT applications.

###  2.1 IoT Applications

Gateway devices will be used by IoT/edge devices and end users to communicate
with computer systems, abstracting away the interactions with sensors and
actuators/effectors located on the edge [38]. The system will communicate with
various and multiple instances of IoT applications (such as healthcare, smart
city, farming, and weather monitoring) or their digital twins to efficiently
provide AI and other autonomic services [39].

Figure 3: A Prospective Model for AI-integrated Next Generation Computing

###  2.2 Resource Manager

Distributed systems, including IoT edge platforms, require adaptive and fault-
tolerant management of resources and scheduling of tasks. The proposed
resource management module maintains the set of available and reserved
resources (the number of CPUs utilised, the amount of memory, the price of
resources, the kind of resources, and how many resources there are) as well as
the desired resources, constraints (e.g., placement) and QoS per deployed
task. Further, the module incorporates data supplied by the provider on the
accessible and scheduled resources, as well as the resource specification
(resource identity, resource category, configuration, data, use information,
and pricing of resource).

When evaluating QoS, the QoS manager figures out how long it will take to
complete a given workload. Priority queues (workloads with an urgent deadline
in execution state) are created for critical cloud workloads based on Service
Level Agreement (SLA) details, which includes details about the highest and
lowest violation probability and penalty rate in the case of SLA violation.
The service manager is responsible for overseeing all aspects of the system’s
operation. With the use of SLA and QoS information, a mapper may assign
workloads to adequate resources, taking into consideration both SLA and QoS.
After allocating the workloads to the available resources, the resource
manager creates a workload schedule by predicting it using AI. In order to
complete tasks within a certain budget and timeframe, the resource scheduler
makes efficient use of the system’s resources, which are predicted via AI/ML
techniques. Finally, wherever possible, the resource manager will be providing
explainable guarantees under uncertainty—potentially using explainable AI
methods—that the proposed adaptation will indeed meet the desired QoS.

###  2.3 Autonomic Model

This future model employs IBM’s autonomic computing model [1], which
emphasises self-healing, self-configuring, self-protecting, and self-
optimizing features.

  * •

Self-healing is aimed at making all required modifications to recover from
defects in order to keep the system running without interruption [27].
Software, network, and hardware errors must not impair the efficiency of the
algorithm or workload regardless of their severity [10]. Any unintended
exception in high resource-intensive applications can cause a software,
hardware or network failure. AI-based systems can leverage a variety of data
sources and sensor data to generate fault models and enable predictive—instead
of reactive—fault detection and maintenance.

  * •

The primary goal of self-protection is to keep the system secure from hostile
purposeful acts by keeping track of suspicious activity and responding
appropriately in order to keep the system running smoothly [9]. To prevent an
attack, the system must be able to tell the difference between what is lawful
and what is not. AI-based prediction systems can be used to achieve this: for
instance, the system could be trained to detect vulnerabilities in the
communications configurations/policies or identify code smells in the user-
submitted functions/lambdas.

  * •

Installing missing or obsolete parts without requiring any human interaction
is the primary goal of self-configuration. Depending on the situation, a
developer may need to reinstall specific components or perform software
upgrades [2]. Self-configuration takes care of the cost of resources and
penalties for SLA violations, which can be predicted in advance through AI/ML.

  * •

Dynamic scheduling approaches are used to match jobs and workloads to the best
available resources in the self-optimizing aspect [27]. The autonomic
element’s input is used to constantly enhance the system’s performance through
dynamic scheduling. AI/ML based adaptive scheduling can be used for data-
intensive applications because it is flexible and can be adjusted to a
changing environment with ease. Further, the impact of different QoS
characteristics on system performance can be measured automatically [8].

Models for complex distributed systems that can self-heal, self-configure,
self-optimise and self-protect have been developed using this idea. Autonomic
elements (AEs) are primarily in charge of managing resources on their own [1].
Figure 1 shows a schematic representation of the many components that make up
an AE system. Interaction between all the AEs is necessary for the sharing of
messages on system performance. AEs complete a necessary sub-task to maintain
the system’s performance based on interaction. There are four stages to the
IBM model of an autonomic system [1]: Monitor, analyse, plan, and execute are
the four steps in the process, which will be supported by AI/ML models to
improve the monitoring, analysing, planning and execution. Further, AI-powered
techniques could also improve the efficiency of the persistence (knowledge)
component of the MAPE-K loop, especially in effectively resolving state
synchronization in a highly-distributed and potentially unreliable
environment.

####  2.3.1 Sensors

Sensors gather data on the QoS metrics of the present state nodes’ performance
[2, 8, 9, 10]. Input from computation elements is first sent to the manager,
which subsequently sends this information to Monitors through the manager
node. Faults (software, network, and hardware), fresh updates on component
status (outdated or missing), and security threats are all included in the
recent developments (intrusion detection rate).

####  2.3.2 Monitor

Initially monitors data from the resource manager node to continually check
performance variances by contrasting AI-based predicted and real outcomes [2,
8, 9, 10]. The threshold value of QoS metrics, which also contains the highest
value of SLA violation, is already recorded in the knowledge base. The faults
(network, software, and hardware), fresh upgrades of resources (obsolete or
lost), security assaults, variation in QoS parameters, and SLA violations are
noted, and this data is transmitted to the next module for more investigation.
Each node has a QoS agent deployed to monitor and predict the performance of
the above-mentioned QoS parameters for self-optimization. Self-protection is
achieved by installing security agents on all processing nodes, which are then
utilised to track down both undiscovered and recognized attacks. After
analysing the system’s current database, additional abnormalities can be
predicted using AI/ML. System invasions and system abuse are detected and
classified as either normal or abnormal utilising its monitor and the system’s
attributes are compared with metadata. Hardening agents for software,
networks, and hardware will be reducing attack surfaces by identifying
corresponding flaws to achieve self-healing and self-protection. When a new
node is introduced to the cloud, the hardware hardening agent scans the
drivers and validates the replica of the original drivers. The new node is
inserted when the device driver has verified it. This node will create a
warning if it is still present in the system. The performance of the software
and hardware components is monitored by agents for self-configuration. The
software component agent retrieves the active component condition for all
software components that are employed on separate processing nodes.

####  2.3.3 Analyze and Plan

When the monitoring module sends data, the Analyze and Plan unit evaluates it
and identifies a strategy for reacting to the alarm [2, 8, 9, 10]. After a QoS
agent generates an alert, the analysis unit begins predicting QoS metrics
associated with a specific node. ‘DOWN’ status is reported for that unit, and
the unit is restarted, and the state of that node is measured. Alternatively,
new resources are added if the node state goes to ‘ACTIVE’. After an alarm is
sent by a hardware or software agent, the analysis unit begins examining the
behaviour of the node’s hardware and software (self-healing). Node ‘N’ should
be set to “DOWN” if an alert is produced during workload execution and
restarted, to measure the state of that node. The execution of the node’s
state switches to ‘ACTIVE’ if execution is continued, or alternatively another
reliable node is chosen. Self-protection begins by examining attack logs once
an alarm is produced by the security agent and a signature is created by the
analysing component. After an alarm is issued by a hardware or software
component, the analysis unit begins studying the behaviour of a node’s
hardware and software components. It is necessary to designate a hardware
component as “DOWN”, reset the failed component, and then start it again in
order to predict whether or not it is “CRITICAL” or “ERROR”. Again when the
data has been processed, this framework takes care of implementing the alert-
related actions on its own.

Further, before any adaptation does take place, the modules will first provide
evidence that the proposed plan will indeed complete successful. This is
achieved using a combination of formal guarantees, which can be derived from
the use of control theory. An AI-model can also be used to predict when the
users might issue a goal update–based on external information or other types
of operational data—and prepare/assess an adaptation plan ahead of time.

####  2.3.4 Executor

A plan is put into action by the executor [2, 8, 9, 10], whose primary purpose
in self-optimization to enhance QoS and execute tasks within a pre-defined
deadline. Using the data from the analyzer, the executor may quickly, cheaply
and efficiently add a new node to the pool of resources. If the resources are
not already in the pool of available resources, then notify the user and
negotiate a SLA before adding a new node from the backup pool of resources
with the least amount of workload, price and power usage requirements. These
aspects can be predicted in advance using AI/ML. A node that is not reliable
should be replaced with a node that is the most stable amongst those
available. To relaunch the node, the current status of a node is stored
(checkpointed). The node is then restarted. If the problem persists, an alert
is subsequently generated.

For self-healing, whenever a new component is introduced, it should be linked
to other components and restarted.

####  2.3.5 Effector

New policies, regulations, and notifications are sent to other computing nodes
via the effector [2, 8, 9, 10], which serves as an interface between the
various computing nodes. Through the effector, the computing nodes can work
together to form a more powerful system. It is worthwhile mentioning that a
system-of-systems approach is likely to be leveraged for such applications;
hence, effectors of a top system might be triggering adaptation of a bottom
system and so on.

####  2.3.6 Knowledge Base

The main aspects of information stored in the knowledge base are the
following: (a) The current and previous states of the system (including
deployed applications, available computing resources, etc.), whose values are
read via the system’s monitors. (b) The desired state of the system, which is
driven by specifications set by the user/admin/operator of the system; they
include both functional requirements, such as the microservices network of
deployed applications, as well as nonfunctional requirements, such as QoS
Service Level Objectives (SLOs) on desired response time, tail latency, target
resource utilization, etc. (c) Current, past and predicted models—as well as
meta models and surrogate models—of the system and its environment generated
via AI/ML as well as the efficacy of the various AI/ML methods used for their
training. (d) Current and past execution plans that are devised by the planner
module and implemented by the executor module. (e) The actual code of the
various interfaces the system provides to enable informed self-adaptation by
autonomically incorporating improved methods for various operating aspects.
For example, new AI/ML, scheduling and resource management algorithms, could
be selected by the self-adaptation algorithm and added in, which would
eliminate the need for the software engineering team to have to patch the
system. (f) Further, the Knowledge Base will maintain pre-stored policies with
predefined configurations to support system management. It is the
responsibility of the system administrator to periodically update the policies
stored in the Knowledge Base to reflect changes in resource scheduling
regulations. A system admin will be replaced by an AI-based autonomic agent to
handle the execution automatically.

Crucially, the knowledge module needs to provide a centralized location for
the various running tasks, which could be executed as threads, processes and
of course across multiple nodes of the distributed cluster, to safely store
and exchange information. This kind of architecture is required for highly
distributed systems; otherwise, direct communication between the various units
will result in dramatic slowdowns due to locking and contention, increase the
attack surface or even worse, into system failure due to synchronization
issues, such as race conditions, that can invalidate information manipulated
by multiple actors. Finally, the knowledge base needs to be replicated,
potentially across multiple reliability zones, to assure business continuity
in cases of hardware and communication failures or even a catastrophe that
knocks down a whole datacenter. As such, distributed consensus and adaptive
data recovery algorithms are required to maintain data validity.

###  2.4 Service Management Layer

There is a database manager in this position (which manages the data of IoT
applications effectively). AI-based systems can be used by Security Manager to
predict and guard against external threats on task execution [40]. Application
data may be securely sent during task execution with the help of a blockchain
service. At runtime, the serverless manager controls the cloud resources that
IoT applications are consuming. With the integration of Serverless data
pipelines with quantum computers, efficient load balancing and dynamic
provisioning may be achieved for the edge computing paradigm. It is the
responsibility of the application manager to control the deployment of IoT
applications and to provide data for the allocation of resources in advance,
which can be achieved using AI/ML. The placement module serves as a bridge
between the application manager and the application placement module.

Four categories of services are included at the bottom layer [40]: function
(FaaS), software (SaaS), platform (PaaS), and infrastructure (IaaS). Function
containers are used to provide a virtual environment for computer systems that
can be dynamically scaled up and down. SaaS uses the notion of virtualization
based on VMs to deliver cloud-based services. Platform as a service can be
provided via Microsoft Azure, Amazon Web Service (AWS), or OpenStack. By
lowering latency and reaction time at the edge devices, fog and edge computing
may be used to deliver the infrastructure service. Orchestrating containers
using orchestration is an intermediary step between deploying containers as a
service and deploying them as software as a service (SaaS). The placement of
IoT applications for dynamic provisioning and management is handled by
application placement, which is a bridge between SaaS and PaaS. Machine
learning and artificial intelligence-based approaches are used to schedule the
cloud resources of PaaS and IaaS [41]. Using quantum technology, the system is
able to perform nonce or Proof-of-work (PoW) computations in a fraction of the
time.

##  3 AI for Computing: Current Status and Open Challenges

It is very important to identify the research opportunities for leveraging AI
and ML in next generation computing for emerging computing paradigms,
including cloud, fog, edge, serverless and quantum computing environments as
shown in Figure 4. This section discusses various new trends and open
challenges in AI-integrated next generation computing.

Figure 4: Emerging computing paradigms

###  3.1 Cloud Computing

It is becoming increasingly evident that the rise of cloud computing and the
rise of AI are mutually reinforcing. As a result, using AI in the cloud can
improve the cloud’s performance, efficiency, and digital transformation [42].
AI in the cloud computing environment is a crucial key to enabling
organisations to become more efficient, strategic and insight-driven, while at
the same time providing greater flexibility, agility and cost savings [43]. As
a result, we turned to industry insiders for their insights about the
expanding importance of AI in cloud computing.

AI and cloud computing may be combined in a variety of ways to enhance cloud
computing. AI tools and software are synched with the power of cloud computing
in order to provide an enhanced value to the existing cloud computing
environments [44]. This combination makes enterprises efficient, strategic,
and insightful. Data and applications hosted on the cloud allow businesses to
be more responsive and adaptable, while also saving money for the company as a
whole [44]. Existing capabilities gain intelligence, and customers receive an
excellent experience, thanks to the addition of this additional layer of AI
that aids in the generation of insights from data [44]. As a result,
businesses may profit from a tremendously distinctive mix. Cloud is like a
video game, which emits an enormous quantity of operating data and telemetry,
much like a Tesla electric vehicle [45]. As a result, AI-based cloud computing
is basically AI Ops, which uses algorithms to make sense of all this data
rather than relying on humans [46, 47]. In the post-COVID future, cloud-
computing investment increased by 37 percent to $29 billion in the first
quarter of 2020 compared to the first quarter of 2019 [48]. Integrating AI and
cloud computing can therefore help businesses get closer to their consumers
while also increasing their operational efficiency [49].

Cloud computing environments and solutions are helping businesses to become
more agile, adaptable, and cost-effective because this significantly cuts
infrastructure administration expenses for corporations [50]. As a way to
handle enormous data repositories, simplify data, improve workflows, and
create real-time insights for day-to-day operations, AI gives companies more
freedom. The operational weight may be shifted from processes and people to
engineering and data [51]. That is why AI is boosting cloud computing in a
variety of ways. The Software as a Service (SaaS) paradigm is currently being
used to successfully employ cloud-based AI [52]. SaaS companies are
incorporating AI into their solutions, which provides clients and end-users
with enhanced capabilities. Another method businesses are adopting AI to
enhance their present cloud infrastructure is through AI as a service [53].
The use of AI makes applications more flexible and efficient, reducing
mistakes and increasing production.

The cloud native paradigm derived from cloud computing has shifted the
traditional monolithic cloud application into light-weight, loose-coupled and
fine-grained microservices [54]. This paradigm can support the applications to
be updated in a much more efficient manner. However, due to the increased
number and time-sensitive features of microservices, their efficient
management can be challenging. AI/ML based solutions can address some of the
challenges, for instance, neural network based approach can be applied to
predict workloads of microservices, and ML based techniques can be utilized to
analyze the dependency of microservices.

The following are various advantages to deploying AI in the cloud:

  * •

Enhanced data management: Data is king in today’s data-driven world, which
necessitates better ways to handle it. An enterprise’s ability to keep track
of that data is a major hurdle [55]. Cloud-based AI tools and apps that
recognise, update, catalogue, and provide real-time data insights to clients.
AI techniques may also be used to detect fraudulent activity and identify
anomalous system trends [56]. Banks and other financial institutions rely
heavily on this technology to remain competitive and safe in today’s high-risk
climate.

  * •

Automation: Intelligent automation can now be implemented throughout a whole
business thanks to the combination of AI and the cloud, which removes the last
remaining roadblocks [57]. Predictiveness is enhanced by AI since algorithmic
models draw on historical data and other patterns to deliver in-the-moment
insights [58]. AI and cloud computing solutions can help businesses go from
semi-structured to unstructured documents cognitively automated while also
pushing the frontiers of effective infrastructure management, resulting in
little downtime and impact [59]. As a result, the cost of doing business is
transformed, and the customer experience is transformed as well.

  * •

Cost Savings: Cloud computing allows businesses to just pay for the resources
they utilise. This saves a significant amount of money compared to the typical
infrastructure expenditures of building and maintaining massive data centres
[60]. Saved money may be utilised to build more strategic AI tools and
accelerators, which can then be used to increase revenue and save the company
money at the core [61]. This will lead to better operational quality and
cheaper expenses.

####  3.1.1 Open Challenges

The following issues that may arise when these two technologies are combined:

  * •

Integration: It is never easy to get started with a seamless integration of
two different technologies. In order to accomplish this integration, companies
must first move all of their apps and technologies to the cloud [57]. This is
no small feat for many companies. Businesses may only begin to consider cloud-
based AI after undergoing such a seismic shift. Thus, the technological sync
is excessively reliant on companies that are implementing tangible digital
transformations of their systems.

  * •

Inadequate data: Large datasets with high-quality data are ideal for AI
technologies. Businesses must make sure that their data is both accessible and
clean in order for AI to be of any use [62]. Because data is often unorganised
or missing, this is a big difficulty. It is critical that the solution’s value
be derived from high-quality data.

  * •

Security and privacy issues: To prevent data breaches, businesses must be
vigilant about protecting their sensitive and financial information from
adversaries, who are likely to target them [63].

This synchronisation of AI with the cloud necessitates tremendous knowledge,
resources, and financial investment if it is to be worthwhile for businesses.
It is only when cloud computing and AI systems are properly integrated that
companies will be able to utilise a wide range of powerful machine learning
capabilities, such as image recognition and natural language processing [64,
65]. As a result, additional businesses will follow suit in the future.
Businesses will require an AI cloud in order to keep up with the rapid
advancements in cloud computing. After successful implementation of it, AI
operations will eventually become the standard approach for cloud management
[47]. The cloud is already a powerful technology, but they believe that AI
will make it even more so. With this combination, data analysis and management
will undergo a radical shift. The marriage of AI with the cloud is a game-
changer and will bring unparalleled value to end-users in a world flooded with
vast volumes of data [62]. Now that cloud computing and AI are more widely
available, they are producing upheaval in a wide range of industries
throughout the globe. It is clear that technology has moved from being merely
operational to one of strategic importance. AI is expected to help the company
tackle new and more visible challenges, as well as open up a new universe for
its potential clients.

###  3.2 Fog Computing

Fog computing was established to supplement cloud computing services because
of the rising use of the IoT and the necessity to handle and store massive
amounts of produced data [66]. IoT applications with minimal reaction time
requirements can be supported by fog computing, which provides basic network
services [67]. It is difficult to distribute IoT application activities
efficiently inside fog nodes in order to fulfil QoS and quality of experience
(QoE) restrictions due to fogs’ scattered, heterogeneous, and restricted
resource nature [68]. Vehicle-to-Everything (V2X), health monitoring and
industrial automation employ fog computing because it provides computing
capabilities near to the user to meet reaction time expectations for these
applications [69]. As a result, these apps create enormous amounts of
information from the widespread use of IoT devices. Because of delays in long-
distance data transmission and network congestion, cloud computing is unable
to meet latency requirements [66]. It provides a network of gateways, routers,
and compute nodes between the source of data and cloud computing centres.
Because of the low latency and energy efficiency, as well as the reduction in
bandwidth required for data transport, fog computing extends cloud computing
[70]. Fog nodes can be used to process sensitive data instead of sending it to
the cloud, which improves security [71]. Using the data generated from various
IoT devices, these applications aim to provide helpful information while also
addressing latency concerns [67]. In recent years, researchers have
increasingly turned to AI to help them analyse large amounts of data for the
aforementioned uses. AI’s Machine Learning (ML) and Deep Learning (DL)
subfields give useful data insights and decision help [72, 73]. Following
that, we are discussing some of the AI-enabled fog computing technologies that
make these applications possible.

For the IoT, 5G signifies more than just a new era of wireless innovation.
More than trillions of sensors, gadgets, and machines are powered by AI and
run autonomously from the data centre to the edge of the network [72, 73]. In
terms of speeding up data analysis and decision making, fog computing and edge
computing are the two best technologies. Many “fog devices” will be networked
and co-located as part of a distributed computing system known as fog
computing [66]. Edge management, data collection, monitoring, analytics, and
streaming all take place at the edge of the network in the fog computing nodes
[74]. While fog computing is capable of connecting a limited number of
devices, this technology has a far greater capability to handle real-time
requests and to aggregate data from a much larger number of sources. Input-
response delay is therefore greatly reduced. We have been able to access
resources of all types, have scalable architectures with the press of a
button, and utilise them from anywhere since Amazon’s cloud was launched in
2006 [75]. Cisco claimed in 2008 that IoT, is the one of the technologies that
will benefit from the cloud, but its roots date back to 1999 [76]. For
example, we can save sensor data and act on it, automate processes using AI,
and react in real time to circumstances that previously necessitated direct
involvement. When IoT was first introduced, it promised to extend to both
professional and personal areas, and how sensorization and communication
protocols had to change to meet these new demands [77]. New paradigms have
emerged as a result of the integration of sensor data and the application of
AI to it. New terms like “Smart home” are being used to describe new
technologies that make it easier and more convenient to manage our homes’
energy use and other aspects of our daily lives [78]. On a broader scale, the
term “Smart City” is used to describe cities, while “Smart Factory” is used to
describe manufacturing and processing facilities. One thing they all have in
common is the utilisation of data and automated decision-making in combination
with automation, which can be easily achieved using AI and ML techniques.

An example of this is altering the configuration of a computer or railway,
putting the brakes on an autonomous automobile, or sending a warning for a
preventative maintenance. It is evident from the examples that decision-making
and action-taking cannot be done on the cloud, but rather on devices that are
closer to the sensors that collect the data. In contrast to cloud computing,
fog computing offers a variety of advantages for IoT applications [79]. First
and foremost, quicker and real-time processing are possible because local
processing is used rather than relying on the cloud. As a result of the large
number of IoT devices already in use and expected in the future, less network
traffic means better communication. Additionally, more apps may be developed
and operated everywhere there is an Internet connection. We need to think how
AI could help in the automation?

####  3.2.1 Open Challenges

Research on application deployment has already been done in several domains,
such as industry and manufacturing, but there are still a number of issues
that need to be addressed.

  * •

Execution Time: For both service providers and customers, time is the most
pressing issue. One of the key motivations for putting software in the fog is
to speed up user reaction time. The time parameter was one of the performance
indicators investigated in the literature [80, 81]. When there are more
demands, the QoS suffers. This difficulty has been partially alleviated by the
presented techniques, but it remains a concern [82]. In the application
placement problem, applied techniques for optimising time performance metrics
in the category of deep learning algorithms [83, 84, 85]. We may be able to
achieve better outcomes if we use different machine learning algorithms and
evolutionary algorithms or novel combinatorial techniques.

  * •

Mobility-awareness: Fog computing’s lack of mobility support may be noticed
when dealing with a large number of mobile users with varying application
needs [86]. Consequently, migration methods and architectures that can handle
a wide range of mobility activities are required. Migrating VMs or containers
is something that has been discussed in a few publications [87]. Moving to a
new location may be expensive, as well. Reinforcement learning (RL) approaches
like Q-learning and State–Action–Reward–State–Action (SARSA) have been used to
study this topic [88], but it remains a challenge in practical contexts where
there are many requests [89, 90].

  * •

Resource Scheduling: Another problem the authors encounter is managing
resources in a dynamic environment like fog, which has a limited amount of
resources and a short reaction time for the user. The Fog environment is less
flexible than the cloud when it comes to resource sharing [91]. Therefore, the
issue of efficient resource utilisation must still be addressed. Resource
allocation was based on a survey of existing research and the use of neural
networks, support vector machines, and k-nearest neighbours (KNNs) [92].

  * •

Energy-efficiency: The amount of energy used if supplied policies and
algorithms are improved, idle fog nodes may be turned-off and energy
consumption can be avoided in combination with QoS and QoE since the
application modules are situated in the dispersed fog nodes [80, 81]. Energy
consumption and cost are influenced by memory, CPU, and bandwidth use which
can be predicted using machine learning methods include K-means, KNNs,
logistic regressions, branch and bounds and Deep Q-Network (DQN) and SARSA
[93].

  * •

Security and privacy: Fog infrastructure is critical to determining the
security of the applications because of security concerns such as information
degradation, identity disclosure, replay, and denial of service assaults [94].
Authentication, encryption, and data integration all need to be implemented in
dynamic computing settings due to the lack of control that users have over
their information [94].

  * •

Fault-tolerance and availability: One of the primary reasons for the
development of fog computing was to improve dependability. When it comes to
fog computing, difficulties like sensor failure, a lack of access network
coverage in a particular region or the entire network, service platform
failure, and a broken user interface system connection are all part of the
equation [72]. Another challenge in the fog environment is to increase the
availability of apps. A heuristic approach to improving service availability
and QoS is to map applications to fog communities and then transitively put
their services on the fog devices’ community, according to the service
placement problem [66].

Images, video, natural language processing (NLP), and robotics are some of the
more recent fog computing applications that are only starting to emerge [64,
65]. Fog computing’s picture placement and processing is one of the most
widely utilised sectors of AI in research and industry, with the goal of
differentiating objects or people from one another and the capacity to
classify and discriminate photos based on image processing algorithms [95].
The use of fog computing for image processing-based applications decreases
response time and improves QoS. Placement in fog settings with effective
scheduling algorithms might be beneficial in circumstances linked to medical
applications that demand precision in image processing and fast processing of
medical data [33]. According to a literature, deep learning algorithms, such
as Convolution Neural Networks (CNN) and Generative Adversarial Network (GAN),
can be used in the image processing area in fog [96].

Another area of interest in the sector is NLP [96]. For sound processing and
recognition, cloud and fog environments are needed to store data. For security
reasons, deep learning approaches with sound imitation might be useful. For
example, scenarios for smart homes and processing and identifying the
homeowner’s speech from outsiders should be done with care and speed, wherein
effective scheduling methods for placement of NLP applications in fog will be
proposed. Techniques from the field of deep learning may be beneficial here.
Industry, trade, agriculture, and health all benefit greatly from robotics,
making it an essential issue for discussion [33]. In circumstances when quick
judgments must be made, the usage of fog environments that employ machine
learning to process and make decisions, may be acceptable. In order for robots
to communicate data, they require an environment like fog that responds
quickly to their commands [97]. IoT defines each robot as an item capable of
interacting with other IoT things and other robots [98]. Literature reported
[98, 97, 99] that deep learning approaches for placement robot tasks in fog,
however further research is needed. In the future, methods and scheduling
algorithms for fog computing’s application placement problem will need to be
established depending on the types and categories of request applications,
according to research and evaluations of literature [100]. An application
placement issue in fog for robotics or simple image, video, audio processing
is an example of this type of difficulty [101]. As a result, the QoS and QoE
will be enhanced using AI.

###  3.3 Edge Computing

Distributed computing has evolved from content delivery networks to become a
generally accepted and commonly used edge computing paradigm that brings
processing and data storage closer to the end user’s location [102][103].
Instant data that is created by the user, and only for the user, requires
compute and storage on the edge, but big data always requires cloud-based
storage [104]. As customers spend more time on mobile devices, businesses have
recognised they need to move key computation to the device in order to service
more customers. The edge computing market has a chance to develop as a result
of this. By the year 2023, it is expected to reach $1.12 trillion [105]. 74
percent of all data will need to be handled on the edge by 2022, according to
Gartner, compared to 91 percent of all data currently being processed in
centralised systems [105].

Customers are more concerned about their privacy and want to know how and
where their data is acquired and maintained. After completing the app’s
authentication procedure, a slew of businesses serve their clients by offering
applications with AI-enabled tailored features [106]. These aid users in
protecting their personal information. Customers often utilise speakers,
phones, tablets, and robots to access AI-enabled gadgets [107]. Multiple
levels of encryption and a dynamic encryption process are required due to the
sensitive and personal nature of the data. Edge nodes facilitate the
construction of a highly distributed architecture and help establish the
appropriate security strategy for each device [108]. There are worries about
latency when data is sent across networks and devices since services are
dispersed at both the network and device levels. Due to this delay, the work
must be done on the fly. Having several endpoints of load balancing is a need
when an application has to be end-to-end resilient and have a widely spread
architecture. Resiliency at the device level is increased by the fact that
data computing services are closer to the mobile device or on the edge
(referred to as a “cloudlet”) [109]. We have to think, how these challenges
can be overcame using AI?

Edge computing is a major enabler for AI, giving high-quality performance at a
low cost. This is the best way to understand the link between AI and edge
computing. We can benefit from the marriage of AI with cutting-edge computers
[110]. Edge Computing helps AI-enabled applications overcome the technical
problems of AI-enabled applications because of the data- and compute-intensive
nature of AI. AI and machine learning systems absorb vast volumes of data to
spot patterns and deliver reliable suggestions [111]. Cloud-based streaming of
high-definition video data results in latency issues and increased costs,
since huge bandwidth is utilised, in AI use cases requiring video analysis
[84]. When ML triggers, decisions and actions must be made in real time, the
latency and dependence on central processing in the cloud are detrimental.
Processing and decision making may be done at the source of data, which means
that actions can be taken at the edge and backhaul expenses can be avoided,
making the edge an ideal location for data processing [112]. Rather of storing
sensitive data on the cloud, the edge stores client location data. Streaming
data to the cloud only includes the most important information and data sets,
leaving the rest of the data behind [113].

Due to their scattered and complicated nature, edge computing networks have
brought several issues when it comes to infrastructure management. There are a
number of activities that must be completed in order to effectively manage
resources. These include workload estimation and task scheduling as well as VM
consolidation, resource optimization, and energy conservation [114]. In
dynamic, fast changing settings and in real-time scenarios, traditional pre-
defined rules, largely based on operation research approaches, have been used
for resource management in the past. AI-based technologies are increasingly
being employed to address these concerns, particularly when decisions must be
made. Approaches including AI, ML, and DL have become widespread in recent
years. On the other hand, deciding where to carry out a work on the edge is a
difficult choice that takes into account aspects such as the amount of traffic
on edge servers and the mobility of users [115]. In order to further on the
element of user mobility, the cache must be able to forecast where the user
will go. For the sake of reducing expenses and energy consumption, it is
located at an appropriate edge server. Reinforcement learning, neural network
models, and genetic algorithms are some of the approaches that are employed
[116].

In the commercial and industrial sectors, the advantages of Edge have quickly
spread. Specifically, the reduction of IT equipment’s growing expenditures on
cloud and network bandwidth. All of the company’s activities take place in
different parts of the world. Only an estimated 1% of the monitoring data is
relevant for business insights like anomaly identification or future event
prediction, despite the fact that the cloud and big data centres are
overflowing with data [117]. Edge delivers high-quality business services
through local processing, analytics, and local devices. This is the
operational efficiency and significance of the edge, since it prevents the
transfer of terabytes of unnecessary data to the cloud/data centres and only
communicates pertinent actionable data to the end user [118]. Every day, new
and creative applications for edge’s capabilities emerge. Edge computing still
has a problem in moving to the last-mile of dispersed networks, but new use
cases in industrial applications show a strong convergence with AI in
particular, offering substantial value to businesses [119]. In the automotive,
construction, process, and manufacturing industries, augmented reality,
virtual reality, and mixed reality are becoming increasingly popular [118].
This necessitates a scalable, highly adaptable, and quick-to-respond computing
infrastructure that is always available. Provides a low-latency experience and
application instances that are near to the end user AI and machine learning
have a plethora of uses in Edge Computing. NLP and CNN are two developing
technologies that are used in a wide range of everyday applications [96].
Smart retail, contact centres, security, and legal assistantship all benefit
from NLP’s ability to parse human voice, recognise handwriting and classify
text. Use cases such as quality control, facial identification, healthcare,
and industrial safety can benefit from CNN capabilities in visualisation
algorithms, which enable to detect faces and other visual data [96].

####  3.3.1 Open Challenges

As compared to cloud, fog, or serverless computing, the problems of edge
computing environments are markedly different. As a result, the edge
environment is plagued by issues related to scalability and performance,
especially when dealing with mission-critical data and applications [111]. It
is tough to keep track of the health and state of each IT component,
especially when there are so many remote edge locations to keep track of, much
alone visualise and analyse their influence on other linked equipment, when
considering the scale issue [120]. Highly dispersed and diverse networks
define edge settings. Because of the disparate nature of the infrastructure’s
components and the high costs associated with acquiring the various skills and
resources required, this creates “edge silos”, which only serves to complicate
matters further [84]. To handle the extremely dispersed and heterogeneous edge
environment, AI-based intelligent software is crucial. It helps to collect and
unify data from many sources and provides a highly abstracted “low touch”
monitoring and administration, which eliminates human involvement. It is also
possible to have entire client security without the client’s involvement
thanks to automated security and responses. You may pick from a variety of
suppliers, avoiding vendor “lock-in”, and switch out equipment with no
negative impact on your business or cutting-edge efficiency [112].

Additionally, real-time performance management between end points, such as
consumers and cloud/data centres, is a critical issue [113]. End-to-end views
and data repositories at all sites are supported by technology tools that
continually monitor performance metrics and data flow. For situations when
edge equipment fails or is unavailable, edge infrastructure has built-in
redundancy to isolate, repair and sustain acceptable levels of functioning
[114]. If you run an edge data centre where multiple teams are in charge of
different portions of the infrastructure, you’ll run into certain
inefficiencies. Advanced correlation and analytics, based on AI, are quite
helpful in this situation for examining, consolidating, and unifying data from
many sources, transforming data into information, and communicating that
information with the concerned roles in the team. Information that can be used
to help automated processes is supplied [115].

The followings are the key open issues of adopting AI in edge computing:

  * •

With tremendous advantages, edge computing has a number of challenges to
overcome. Edge computing adoption is being stifled by some of the causes
listed above. Edge computing has no legal, societal, or ethical framework for
the use of AI. We need to do more study on the present benchmark tools and
techniques. Newer technologies have a hard time being integrated into existing
legacy industrial systems since they aren’t modular. Information security
issues and a lack of integration testing with new entrants further limit
technology use [116].

  * •

Small and inexpensive, most edge devices do not require third-party API
authentication, leaving them open to exploitation [104]. They are designed
with simplicity of use and low cost in mind, not security in mind. Edge
devices that gather personal information, such as email addresses, phone
numbers, health information, and credit card data, are on the rise as a result
of specialised apps [121]. The necessity for an AI based security framework
before beginning large-scale and sensitive edge initiatives naturally causes
IT and network administrators to be apprehensive [117].

  * •

For AI-integrated edge workflows, new software frameworks and toolkits are
needed [111]. Working with heterogeneous hardware and platforms, as well as
the resources available in a workflow, will be supported by these software
frameworks [96].

  * •

When it comes to the administration of edge devices, there are no set
standards and regulations that apply universally. The complexity of the IoT
network architecture increases as more IoT devices are added to the edge
[118]. There is still much to learn about the local consequences of IoT
standards for different companies across different geographies, despite the
fact that the US and UK governments have produced them. Organizations require
a framework of regulations and criteria before they can make the decision to
shift their data and application assets to AI-integrated edge computing
environments, according to their perspective [115].

  * •

Only by fully integrating edge computing into existing cloud architecture can
its full potential be realised using AI, making edge computing the crucial
missing connection between data sources (the devices) and cloud computing
(core network) [107] because edge nodes have a limited storage and computing
capacity [110].

  * •

The main strength of this system is its tight interaction with the cloud. As
more businesses move to a multi-cloud environment, it becomes more difficult
for the cloud to set up a redundant AI-integrated edge network to accommodate
incoming data traffic from numerous nodes [108]. High bandwidth needs and
redundant data reporting and routing requirements for IT managers have
increased the search for cloud suppliers capable of meeting these demands
[111].

  * •

Additionally, establishing an AI-integrated edge computing environment
requires an initial investment in edge-enabled software frameworks and
hardware [109]. The fact that this investment typically has to compete with
other company objectives makes it a bottleneck. In developing countries, this
problem is exacerbated.

  * •

Many small and medium-sized businesses (SMEs), IT managers, and government
decision-makers are unaware of the possibilities and applications of AI-
integrated edge computing [84]. It may take some time for nations in Asia-
Pacific that are still learning about cloud computing to adopt its
capabilities for edge computing. Micro data centres, rather than edge
workloads, are preferred by most service providers in emerging economies, as
they are more cost-effective [112].

  * •

With the arrival of 5G, billions of devices will be able to communicate with
each other, and the network will see a rise in the rate at which connected
devices are added and removed [105]. IT managers’ judgement in implementing
edge computing will always be questioned and adoption will never achieve its
full potential without a standard, proven, and recognised edge monitoring
technology [113].

  * •

Faster R&D interventions and innovations in security, governance and
standards/frameworks are the road to alleviate the difficulties for the
adoption of AI-integrated edge computing [114].

Satellites in low-Earth orbit (LEO) are being built by private firms including
SpaceX and Amazon to give worldwide broadband internet access [122]. It will
be vital to determine if and how edge computing principles could be
implemented in LEO satellite networks as the group of subscribers to such a
access network increases.

###  3.4 Serverless Computing

When it comes to designing cloud-native apps, serverless computing is becoming
increasingly popular. Serverless is a cloud computing paradigm that abstracts
away the management of operational aspects [123]. Because developers no longer
have to worry about maintaining infrastructure, serverless computing is likely
to expand considerably quicker [124]. Because of this, cloud service providers
may more easily manage infrastructure and automated provisioning with
serverless computing. The time and resources required for infrastructure
management are also reduced as a result of [125]. It is the purpose of
serverless computing to guarantee that the finest serverless technologies are
used so that the investment is minimised and the return is highest [126].
Serverless computing and infrastructure are characterised by the following
terms:

  * •

Functions: Using event-driven models, serverless functions are implemented in
serverless computing. Because the code is automatically executed as events
occur, they are able to speed up the development process [127]. As a result,
numerous services can be linked to the present application. Using these
features, you may effectively create the pay-per-execution model [128]. It is
billed for the time and resources used on executing code under this paradigm.

  * •

Kubernetes: Developers have the option of bringing their own containers to
Kubernetes through Serverless Kubernetes [129]. In Kubernetes-managed
clusters, these containers may be automatically scaled up or scaled down. In
order to deal with exceptional traffic situations and fluctuating workloads,
this automatic scaling function is activated.

  * •

Workflows: A low-code or no-code approach is used with serverless workflows
[130]. It is an aim of this method to reduce the planning overheads associated
with several activities at once. With these processes, developers may connect
various cloud and on-premises services. Serverless computing has the
capability to learn new APIs or standards, so interactions do not need to be
coded [131].

  * •

Application environments: Both the back-end and front-end of a serverless
application environment are hosted on a dedicated server service. Fully-
managed services via dedicated servers assume responsibility for the
application’s scalability, security, and compliance monitoring [132]. As a
result, running an AI-based application on a serverless computing platform is
significantly simpler, as serverless computing meets the dynamic scalability
and security requirements of applications while still conforming to industry
standards [133].

  * •

API Gateway: An API gateway that is both centrally controlled and entirely
managed is achievable with a serverless API gateway [37]. This application
makes it feasible to administer, secure, and analyse APIs on a global scale.
The management of authorisation and other services (such as content and user
services) is therefore made simpler for a serverless API gateway. Serverless
computing infrastructure enables automated API support using AI and database
connectivity for every service that requires it, as stated previously [134].

Platforms across the board are embracing AI because it is the future of
technology. We’ve been able to make better, faster judgments because of these
AI-powered platforms. They’ve changed the way businesses do business, the way
customers interact with them, and the way we gather and analyse business data.
Complicated machine learning systems have a significant impact on the
productivity and efficiency of developers [124]. A serverless design, on the
other hand, addresses most of the issues that developers experience. Using a
serverless architecture, the machine learning models are properly handled and
the resources are effectively managed [135]. As a result of this design,
developers may spend more time and resources working on AI model training
rather than server infrastructure management.

Complex challenges typically need the development of machine learning systems.
They analyse and preprocess data, train models, and fine-tune AI models, among
other things. As a result, APIs should be able to run smoothly [136].
Serverless computing and AI should be used to ensure that data storage and
message delivery are uninterrupted. Machine learning models may benefit
greatly from serverless architecture, which offers a wide range of options and
advantages [137]. Virtually little administration is required to run any form
of application or back-end service. As incoming requests of any traffic volume
come in, the infrastructure provider allocates its own computing execution
power accurately.

AI/ML integrated serverless architecture will have following merits:

  * •

Fair pricing: Serverless design makes execution-based pricing possible, so you
only pay for services that are really being used [138]. As a result, the
pricing model is more flexible and the cost is significantly reduced.

  * •

Independent work: Serverless computing enables the development teams to
operate autonomously with little intervention and delays. Models are viewed as
distinct functions because of this. Invoking this function has no impact on
the rest of the system and can be done at any time [37].

  * •

Autoscaling: This feature frees up the developer to work on other projects
while the system adjusts itself to the changing scope [139]. Storage
prediction is no longer necessary when using autoscaling because developers
may make changes on the fly.

  * •

Pay-as-per-usage: Using a new model called “pay-per-use”, customers only pay
for resources when they really use them. You don’t pay for the amount of
servers with serverless computing, but rather for the use of services [140].
Combined with the scale-to-zero feature of serverless, one just has to pay for
the number of executions and the length of time resources are utilised for.

  * •

Hassle-free server management: Serverless computing provides backend services
that may be accessed only when they are needed, freeing users from the burden
of managing servers [141]. A serverless service eliminates the need for the
user to be concerned with the infrastructure that underpins the service. With
serverless backends, service providers don’t have to modify their setups if
they want to raise or reduce the amount of bandwidth they are reserving or
paying for [139]. It was difficult and expensive for web developers to own the
hardware necessary to run a server before the introduction of the Internet.

  * •

High availability: Serverless programmes have become more popular due to their
built-in availability and fault tolerance. No need to construct services that
will deliver these features to your application, therefore you don’t have to.
Your company doesn’t need to invest in new capabilities because they are
constantly available [142].

By reinventing automation and enhancing the corporate environment, AI has
taken over today’s life and made it easier. Machine learning algorithms on
serverless architecture may be used in a variety of ways to make jobs easier
and data more accurate [139]:

  * •

Applications that employ GPS gather user data, such as their location and
their purchasing habits, to provide suggestions about their preferences or the
next thing they should buy. AI assesses the frequency of alerts and suggests a
number of options that the app users may be able to bear and enjoy before
turning off the notifications. Using this method ensures that clients find the
material useful and enriches the user experience [140].

  * •

Using AI models, it is possible to examine a customer’s financial viability
before recommending an increase in purchasing power. Prior to requesting any
further information, the system will run a credit check to determine their
creditworthiness. As soon as all of the prior invoices have been paid, the
system decides if the transaction should go through or be put on hold.

  * •

As part of logistics, it is important to keep an eye on the routes and
determine how traffic overloads influence customers. In order to help
businesses make better decisions and enhance customer service, AI analyses the
routes and proposes alternate routes [136].

####  3.4.1 Open Challenges

The following is a quick description of the open issues and challenges that
serverless computing presents for AI applications:

  * •

Vendor locking: If a company has committed to a cloud-based provider that
provides technological implementation, switching suppliers will be difficult.
The lack of industry-wide standards is responsible for around half of the
challenges to cloud computing adoption [143]. To understand the consequences
of vendor lock-in, no amount of investigation or study can be relied upon.
Those who fail to keep an eye out for traps end up falling prey to them. In
addition, a serverless interface must be carefully monitored because of the
multiple risks it presents [144].

  * •

Switching vendors: Lock-in circumstances can be seen in two different ways,
depending on who is looking at the event. In their opinion, the problem with
serverless computing today is not the programme itself. Another contributing
cause is serverless computing, a new and fragmented technology that now has a
younger audience that is overly reliant on it [145]. Businesses should only
agree to a platform after conducting comprehensive research and receiving
multiple offers from rival service providers. A single cloud may be all you
need instead of hurrying to implement a slew of different ones.

  * •

Less transparency: As the backend infrastructure is handled by an outside
company, there is less transparency about how things actually function. The
program’s inner workings may be obscured, especially if it interacts with
other applications. Here are a few examples of how this may be put to good
use: The security measures associated with an external PaaS service connected
to your application, for example, are generally not well known [146].

  * •

Others are in-charge of infrastructure management: Because our infrastructure
is in the hands of a third party, it is more difficult to have a comprehensive
understanding of the entire system. Using end-user-targeted devices under this
paradigm, infrastructure is under the control of another firm, malware can
nonetheless infiltrate your environment [147].

  * •

Sustainability: An increasing amount of data generated on the edge is being
submitted to the cloud for ML training/inference; the overall transmission
energy is around 30% of the total energy requirements of datacenters globally
and rising fast. Research is required to ensure the upcoming serverless
paradigm is sustainable with a focus on power-off techniques, increased
computational density via smart workload consolidation, submitting the
kilobytes-long function to the data vs., submitting terrabytes of data to the
function, and effectively combining serverless edge resources for multitenant
clients—e.g., by sharing artificial neural network layers with an acceptable
tradeoff in accuracy [148].

AI has revolutionised market research and customer behaviour. Customers’
preferences are recorded and analysed by an AI model, which then shows their
customised content. Serverless computing simplifies the AI development process
by removing the need for a dedicated server. As a result, serverless
architecture entails handing over the management and monitoring of the
infrastructure to a third party. That is why it is a good idea to work with a
reputable cloud service provider. To guarantee that the infrastructure works
smoothly, the cloud provider must have handled multiple comparable projects
and have expertise in hosting and handling AI/machine learning and serverless
architectures.

###  3.5 Quantum Computing

Quantum computing promise is to be the one technology that has the potential
to fundamentally alter AI. This section introduces the capabilities of quantum
computing and its potential influences on AI and the economy [149]. The
consequences of this approach to computation could affect a considerable range
of aspect of intellectual and economic activities on our societies. With the
enormous influence is AI having, all across the world, the combination with
Quantum computing may have a multiplier effect to trigger a revolutionary
effect on AI.

Quantum computing utilises a novel approach to data and information
processing: Information, encoded in the quantum states of quantum systems, is
processed accordingly to the law of quantum mechanics opening up some
opportunities that are not available to the classical way of processing
information. For example, quantum superposition and quantum entanglement
[150]. Quantum entangled is the property of quantum systems of limiting the
amount of information an observer may obtain on parts of a global quantum
state, making it impossible to provide a complete description from the
knowledge of only the component states. The term “superposition” refers to the
possibility of _combining quantum state_ in order to produce another valid
quantum state.

Previous features of quantum systems trigger, from one side, the power of
quantum computing (if sufficiently shielded from interactions with
environment), but represent also the main limitations that do not allow an
efficient simulation of quantum systems by present computer systems, even AI-
powered supercomputers. In fact, the scaling of the phase space within which
composite quantum systems evolve growths exponentially with the number of
component systems.

The unit of information used by Quantum computers is the qubit, which replaces
the bit used in classical computers. The state of a qubit $|\psi\rangle$,
which could be an atom, a photon, a circuit, etc., can be represented,
mathematically, as a vector in the complex Hilbert space [150], with two
mutually orthogonal basis states $\\{|0\rangle$, $|1\rangle\\}$ as follows

$|\psi\rangle=a|0\rangle+b|1\rangle,$ (1)

where $|a|^{2}+|b|^{2}=1$, and $a,b\in\mathbb{C}$ are Complex numbers. The
exploitation of quantum superposition (See Eq. (1)), and quantum entanglement
is what makes quantum computing considerably more powerful for certain tasks
than classical counterpart [150].

The simulation of quantum systems has been the original scope motivating the
endeavour to build a quantum computer [151], but it has only been after the
discovery of quantum algorithms able to achieve practical goals that the
interest in building this devices started to attract increasing attention.
After the seminal works to formalize the concept of quantum computer [152],
several algorithms followed that allowed to achieve tasks that were considered
hard for classical computers. The discovery of the Shor’s algorithm [153]
provided an efficient solution for factoring large numbers, that had critical
implications for crypto-analysis, boosting studies in both quantum computation
and quantum cryptography. Running effectively the Shor algorithm on a working
quantum hardware, however, it would require a level of accuracy in
implementing register initialization, quantum operations on multiple qubits,
and storage of quantum states that are not yet achieved by current state-of-
the-art devices [154]. It is also worth to mention that quantum computers have
their own limitations. For example, it is not expected that they can
efficiently solve NP-hard optimization problems [155] and, coming to
searching, the speed-up offered by quantum computers scales quadratically with
respect to the time needed by a classical computer (Grover’s algorithm [156]).

Building a quantum computer is, in fact, not an easy task: as experimentalists
know pretty well, the advantages of quantum computing, offered by features
like quantum superposition and entanglement, tend to vanish exponentially
faster with the size and complexity (i.e., the number of quantum systems
involved) of the hardware. Nevertheless, in recent years, we have seen a
spectacular increase in the interest of major high-tech players (IBM,
Microsoft, Google, Amazon, Intel, Honeywell), and a flourishing of many young
companies aiming at proposing solutions for quantum computing, with various
core technologies employed, ranging form superconducting devises [154], to
trapped ions [157], to integrated light circuits [158]. These are just some of
the many companies that are today financing quantum initiatives and are
interested in developing this technology. Despite the difficult challenges
ahead, Google AI group has made significant progress during the recent years
[154], achieving what is known as quantum advantage building a programmable
quantum computer, named Sycamore. Similarly, IBM has recently announced the
first quantum computer to pack more than 100 qubits in their hardware, _Eagle_
chip [159], representing only a first step of busy research and engineering
program during which the tech-giant is planning to push figures to more than
1000 qubits by 2023. However, as said, the challenges to preserve the delicate
features of composite quantum states rely on the ability to shield these
devices from the external environment to allow coherent quantum evolution to
take place under the presence of even very small amount of noise. For this
reason these devices need ultra-low temperature of fractions of Kelvin, which
pose challenges also for designing the appropriate materials able to perform
well at such low temperatures.

####  3.5.1 Open Challenges

While universal quantum computers remain the long term challenge of quantum
computing, Noisy intermediate-Scale Quantum (NISQ) devices are a foreseeable
target to achieve in the forthcoming years. With such devices physicist may
start to effectively simulate complex composite quantum systems, and should be
able to study exotic quantum states that have not been accessible in physics
laboratory yet.

For the next step, once NISQ devices will be reliable and well developed, we
will need to overcome the limitations imposed by the presence of noise during
computation, by supporting the main computing unit with effective quantum
error correction (QEC) circuitry. That may open road towards fault-tolerant
quantum computation that will need to involve thousands and more qubits. In
fact, QEC requires a considerable cost in terms of the number of qubits and
logic gates to be implemented. The road towards the implementation of complex
operations like those needed by the Shor factoring algorithm is still long but
while the research is focused on improving the performance of quantum devices
and the optimization of quantum operations, numerous entrepreneurs are also
interested in producing quantum software solutions. Consequently, many
investors are expected to invest in start-up companies that are revolving
around quantum computing technologies, and in perspective the interest in
quantum computing is likely to increase.

Pharmaceutical investors’ interest in quantum computing has sparked. Many
sectors can benefit from quantum computers and commercial solutions. The
financial industry, healthcare, genetics, pharmacology, transportation,
sustainability, and cybersecurity are all direct beneficiaries of quantum
computing [160]. Quantum computing’s potential has been picked up by the
banking industry. Financial analysts frequently make use of quantum
computational models that incorporate probabilities and assumptions about how
markets and portfolios operate. To do this, quantum computers might help by
processing data faster, running better foresight models, and balancing
conflicting options more accurately. They might also assist in the resolution
of complex optimization problems, such as portfolio risk optimization and
fraud detection [161].

Quantum algorithms in IBM’s Cloud Computing platforms outperform classical
Monte Carlo simulations, according to a research the company just presented.
There is a lot of potential for the healthcare business to profit from quantum
solutions. Quantum computing might lead to improved approaches to personalised
medicine by allowing quicker genomic analysis to create personalised treatment
strategies specific to each patient [162]. Genealogy research generates a lot
of data. As a result, analysing DNA information requires a significant amount
of processing power [163]. Currently, companies are reducing the costs and
resources needed to sequence the human genome; but, a powerful quantum
computer might sift through this data considerably faster, making genome
sequencing more efficient and scalable [164].

Another area where drug development might benefit from quantum computing is in
the field of protein folding [165]. This might help speed up drug discovery
efforts by making it easier to predict the effects of pharmacological
compounds [166].

A crucial aspect where quantum computers, and the promise to build one, have
had considerable impact is in the field of security and cryptography. Public-
key cryptosystems are the foundation of today’s era of communication. Rivest-
Shamir-Adleman (RSA) encryption is in fact the most common cryptosystems for
securing transmission of data over networks, and its working mechanism and
security requires factoring large prime numbers, beyond the capabilities of
current classical computing limits. However, as mentioned above, quantum
computing capabilities, exploiting Shor’s factoring algorithm may renders such
encryption models obsolete. This has led to increasing research, and
investment, over the last decades to build safe cryptosystems in a quantum
computing era, and the projections for the next years show this interest to
grow, e.g., Toshiba’s quantum cryptography revenue target is $3 billion by
2030 [167]. In the meantime, while the efforts to design and implement
effective quantum-key distribution (QKD) protocols expand, the National
Institute of Standards and Technology (NIST) has also issued recommendations
for post-quantum cryptography standards [168]. It has begun a process to
request, assess, and standardise one or more public-key cryptography
algorithms that are resistant to eavesdropping performed by quantum hardware.

Quantum computers have also been proposed for environmental applications, in
the hope that quantum computing may open up new avenues for dealing with
climate crises, identifying and optimising process that may help cope with
global warming and other climate change effects [169].

####  3.5.2 Quantum Artificial Intelligence (QAI)

Quantum computing is more effective than classical computing in managing large
amounts of data. A quantum algorithm is a mathematical algorithm that executes
on a realistic model of quantum computation; the quantum circuitry of
computation is the most often used model. The state of a quantum computing
system can be seen as the information encoded in the physical quantum state
supporting the specific implementation. Quantum information theory is based on
these fundamental object, quantum bits (the unit of information), quantum
gates (the devices that operates on quantum bits), and quantum channels that
connect gates and circuitry to preserve quantum superposition and
entanglement. Quantum computers can handle and process exponentially larger
amount of data than conventional computers can, because they intrinsically
incorporate and manage the tensor product structure of composite quantum
systems, which the number of parameters needed to obtain a complete
description scale as $2^{N}$, where $N$ is the number of qubits. So, for
instance, if $N=100$ the vector space in which the quantum system evolves
would have a dimension that is $2^{100}$. That means that while we may need a
$100$ qubits on a quantum computer (assuming that we do not need error
correction) to describe the evolution of composite system, the same described
on a classical machine, would require $2^{100}$ parameters. It is then clear
that quantum computers are intrinsically prepared in managing the evolution of
systems described by a large number of parameters.

Quantum devices, coherently controlling quantum superposition and
entanglement, can exploit _quantum parallelism_ , i.e., they can
simultaneously _explore_ multiple evolution. For example, in classical
decision the problem is often represented as a _decision tree_ , where the
evolution of the decision path is determined by a binary choice and the state
of initialization of the register. This method becomes less effective when the
creation of branches form each decision split-point slows down because the
time needed to obtain an outcome to decide the split is too long. Quantum
devices, exploiting quantum superposition to initialize the input register,
and quantum evolution (that coherently preserves superposition) may explore,
simultaneously, the various possible branches of a decision, speeding up
considerably the application of these kind of decision-tree approaches. It is
still difficult to provide a certain foreseeable future for the development
and to know precisely how and when we will have a complete and deep
application of quantum advantage, however it is reasonable to say that the
exponential speedup of quantum computing will involve all sort of problems
with large amounts of data to manage, like pattern recognition, or training in
machine learning models.

Training of machine learning models, like in reinforcement learning base much
of their effectiveness on the speed at which the _agents_ learn, interacting
with the environment: They interact, obtain some feedback, and adapt
(learning) their behaviour on the base of the feedback received. This approach
has been confirmed, e.g., in a recent experiment [170] where not only the
training of agents has been improved and accelerated by using a quantum
channel, but it has been also integrated to implement a hybrid
quantum/classical scenario, using a very promising integrated nano-photonic
processor, where classical communication are used for tuning and to achieve
the optimal control of the learning progress.

Clearly, if the computational speedup is exponential, as it is for quantum
computing vs classical, when dealing with large amounts of data, such a
speedup may have application on many different aspects of the development of
AI. Figure 5 provides a clear view of quantum computing with AI for modern
applications.

Biometric recognition and autonomous driving are two critical examples that
can utilize QAI for processing workloads. The fact that quantum computers can
process more data in a shorter time than traditional computers has revealed
the concept of QAI [171]. An example scheme for QAI is given in Figure 5. QAI
involves combination of Quantum Computing and AI to achieve superior
performance results compared to classical AI [172]. Reinforcement Learning
(RL) is a well-established branch of ML that aims to maximize the reward by
trial and error by means of an the agent [139]. It is certain that combining
RL with Quantum Computing will lead to great advances in computing systems.
With quantum computers accelerating machine learning, the potential for impact
is certain to be enormous[173]. Applications of quantum AI for quantum search,
quantum game theory, quantum Algorithms for decision problems, algorithms for
learning are shown in the Figure 5.

Figure 5: Overview of Quantum Artificial Intelligence

The ability of quantum computing to execute a task quickly, may be helpful for
AI systems employed, e.g., in problems related to autonomous driving natural,
natural language processing (NLP) algorithms [174] and, in general, in tasks
where classical approaches are extremely time-consuming and expensive.
Characters and words are the basis for the current algorithms. The idea of
becoming “meaning aware” is a goal of quantum algorithms [175]. To build real-
time speech patterns, these algorithms may use phrases and paragraphs. It is
important to note that predictive analytics are a key AI application and
commercial use case. Massive amounts of data can be used to train AI systems
that are adept at machine learning and deep learning. However, complex and
ambiguous issues such as stock market projections and climate change control
systems require unique data created by quantum principles employing
entanglement and superpositions [169]. New discoveries in Artificial
Intelligence algorithms intended for quantum computers,, or Quantum Artificial
Intelligence (QAI), are expected to deliver the critical breakthroughs
required to advance the science of climate change. Improvements in weather and
climate forecasting as a consequence of this research are predicted to have a
cascading effect on a wide range of socioeconomic advantages. For example,
NASA has established the Quantum Artificial Intelligence Laboratory (QuAIL),
which is dedicated to investigating the possibilities of using quantum
computers and algorithms to machine learning problems in NASA’s missions.

Nanotechnology and nanoscience may also be integrated into AI for very small,
microscopic devices at molecular, atomic, and subatomic levels, thanks to
quantum computing. Quantum physics finds use in nanotechnology. These are only
a few examples of quantum computing’s impact on AI and machine learning [176].
Machine learning applications for quantum devices are already being developed,
in the hope to employ quantum computing to speed up the training of machine
learning models and produce more efficient algorithms for learning [172].
Machine learning and artificial intelligence are likely to benefit from
improvements in quantum computing technology even before a comprehensive
quantum computing solution is ready. Hence, the field of Quantum Machine
Learning (QML) is expected to pick up, followed by its autonomic expansion,
Adaptive Quantum Machine Learning, which will be able to leverage quantum
computing to adaptively achieve self-learning.

Open-source quantum machine learning library TensorFlow Quantum (TFQ) is
available from Google [177]. Cirq is integrated with TensorFlow and provides
high-level abstractions for the design and implementation of both
discriminative and generative quantum-classical models by providing quantum
computing primitives that are compatible with existing TensorFlow APIs, along
with high-performance quantum circuit simulators. Quantum computing has,
indeed, the potential to transform AI in a number of ways. Constraint
resolution, uncertainty handling, and constraint fulfilment will all be
improved by quantum computing, as will adaptive machine learning and spatial
and temporal reasoning [178]. Even if quantum computing is still in its
infancy now, from a commercial and economic standpoint, it is an excellent
moment for startups to join this path. The future of our economy will not be
decided by cryptocurrencies, but rather by quantum computing solutions [179].

##  4 Modern Autonomic Computing with Embedded Intelligence

This section discusses the new research developments related to autonomic
computing with embedded intelligence. New advances in intelligent edge,
intelligent things and sensors as actuators are detailed here.

###  4.1 Intelligent Edge

IoT is bringing billions of new gadgets online, creating an unprecedented
amount of data that will be challenging to manage. Over 75 billion IoT devices
are expected to be in use by the year 2025, according to research [180].
Businesses are developing and installing more and more things meant to improve
the end-user experience and also generate massive amounts of data, such as
linked automobiles, smart metres, and in-store sensors. This new data,
meanwhile, requires real-time collection, management, and processing [181].
Exactly how will this happen? One approach to go forward may be through the
use of edge and fog computing [182].

Edge computing is expected to get a huge amount of attention than fog
computing in the next years, but what exactly is it? As opposed to typical
cloud computing, in which data is stored and handled in a central location,
edge computing processes data on-the-fly [183]. The cloud and edge are not
mutually exclusive in fog computing, which means that some computation can be
done in the cloud, while other parts are handled by edge devices.

As a result, the data sent between linked devices might take too long, and
edge computing utilises significantly less network capacity than traditional
computing. Time can be saved by processing it locally on the device or inside
a local network [66]. Edge computing, on the other hand, might provide cloud
computing with much-needed assistance in dealing with the massive amounts of
data generated by the IoT and other connected devices [169].

In both fog and edge, emerging IoT devices generate and transfer data, and the
processing capacity of those devices is used to accomplish operations which
might normally be carried out in the cloud. Both fog and edge allude to these
new IoT device locations in the network. As a result, they help organisations
lessen their dependence on the cloud by sending data to analytics platforms,
where it can be analysed and turned into useful information. Corporations may
cut down on network latency by using edge and fog technologies to reduce their
reliance on cloud platforms for data analysis. As a result, you will be able
to make data-driven decisions faster. In addition, because edge devices lack
storage capacity, they must send data to the cloud when real-time processing
is finished so that analytics may be conducted on it.

With today’s cloud computing, bandwidth, and processing power, business
communication network focuses primarily on supporting all of your distant
applications, as well as offering infinite storage space. That will eventually
change. In order to get the most out of data, it must be processed in real
time, at the edge [66]. Looking for future, network infrastructures must be
more adaptive and willing to manage a far greater number of smart devices than
they currently are now. Having the decision-making process near to where the
data is created is essential for real-time intelligence. For example, self-
driving cars or self-maintaining smart manufacturing equipment can make fast
judgments on the go [184]. Real-time engine performance data generated by
sensors installed in aeroplanes can be used to take preventative action before
the aircraft returns from the sky. The savings might be substantial. A
company’s ability to deliver processing power and an intelligent environment
will increase as it expands its network of corporate endpoints.

We will need quicker and more reliable data processing as our demand for data
grows and billions of devices are linked to the network. Despite the benefits
of cloud computing, the development of IoT and mobile devices has necessitated
ever higher bandwidth requirements [185]. Cloud computing isn’t required by
every smart device, and avoiding transferring data back and forth over the
cloud is a good idea in some circumstances. The edge may help enterprises
become more nimble, decrease expenses, minimise latency, and better regulate
network capacity [186].

How to provide enough computing power for intelligent applications at the edge
has become a serious challenge. Intelligent edge is a promising way that
pushes intelligence to the Edges of Internet, which has played the role of
intelligent decision-making in many aspects of edge computing, including task
offloading, edge caching, and resource scheduling. Among them, edge offloading
is a distributed computing paradigm that provides computing services for edge
caching, edge training, and edge inference. By integrating methods such as
Distributed Machine Learning (DML), Deep Reinforcement Learning (DRL) and
Collaborative Machine Learning (CML) into the edge computing, it is beneficial
to cope with the explosive growth of communication and computing of emerging
IoT applications [187], and achieve the energy-efficient and real-time
processing [188].

Instead, single pass AI techniques that can operate on resource-restricted
environments have been proposed. For instance, an alternative to traditional
machine learning is data-stream mining. This ML paradigm treats its datasets
as individual datapoints coming in one at a time, while performing adapting
learning with a finite memory budget. From an autonomics perspective, data-
stream mining also leverages the notion of adaptive concept drifts, i.e., to
save resources on the edge, the data-stream model is retrained only when its
performance crosses bellow a threshold [189].

###  4.2 Intelligent Things

Intelligence integrated in a technology relates to the capacity of a product
to analyse and consider its own performance. In addition, it must be able to
deal with the workload and its own working conditions [190]. As a result of
this, the overall experience of the end user is enhanced. When building a new
products/services, it is important to keep in mind the notion of self-
evaluation of the product based on data from embedded sensors [191]. A
business intelligence system must be at the heart of an integral model to
product or service introduction. Employing an embedded intelligent system and
a machine learning algorithm model is a major benefit [192]. This may be
assessed by looking at how well it performs in areas like launching smart
product systems and setting up smart business services, to name just two. With
the help of this cutting-edge infrastructure, it is possible to better
understand and anticipate how the business landscape will shift in the future
[193]. Because of this, it is an area where human analysts typically fall
short. As a result, machine intelligence capabilities based on sensors
integrated in current gadgets and goods have become a need in today’s
corporate environment [194].

The convergence of emerging applications and machine intelligence capabilities
has led to an evolution in this process [190, 191, 192, 193, 194]. Embedded
intelligence is increasingly being used to design the future IoT:

  * •

AI: Human-machine intelligence synthesis is what this term denotes. In a
specific gadget or service, the ability to make choices like the intellectual
is possible.

  * •

Data Integrity: Device history may be tracked using blockchain, a business
intelligence application or innovation. Blockchain is made up of strings of
interconnected block headers and blocks bodies [195]. The block body consists
of all transactions in the block. The block header, on the other hand, is
generated using the hash value, timestamp of the previous block, and a Merkle
root of the transactions it contains. Therefore, each block is created using
the hash value of the previous block and linked to each other. Interfering
with any block will change the hash value of that block and thus all blocks
will be affected. This promises the availability of Blockchain technology in
protecting the integrity of data [196]. Blockchain technology, apart from
cryptocurrency (e.g., Bitcoin), has been used to further enhance and optimise
existing solutions such as cloud storage (e.g., [197]), authentication (e.g.,
[198]), health-care (e.g., [199]), and more. Integrating blockchain allows to
have a clear audit trail of the data and models used to verify the machine
decision process, which will lead to increasing the device’s trustworthiness
[200]. The latter is of extreme importance in machine-to-machine
communication. Furthermore, running AI code over Decentralized Autonomous
Organization (DAO) with smart contracts attached to it limits catastrophic
risk scenarios by limiting the action space.

  * •

Smart Healthcare: Utilizing IoT in the healthcare business may pay out. As a
result, healthcare would be even more widely available, and progress could
proceed at a rapid pace.

  * •

Predictive Maintenance: Predictive maintenance is a notion that has emerged as
a result of the growth of the Internet of Things. This means the practise of
adding sensors to household appliances so that they may send out notifications
when they need to be serviced.

From natural language processing, face recognition, bio-medicine to autonomous
driving, more and more intelligent applications are being deployed on IoT
devices [201]. Due to the slow hardware development in small-sized equipment,
the contradiction between the limited computing capacity of IoT devices and
running complicated AI applications cannot be efficiently solved in a short
time [202]. In addition, there remains significant challenges in developing
system-level, algorithm-level, architectural-level or infrastructure-level
technologies for embedded intelligence, e.g., real-time decision making,
energy-efficient Deep Neural Network (DNN) training and DNN inference, and
security deployments.

###  4.3 Things as Sensor-Actuator Network

Cyber-Physical Systems (CPS) are the next generation of embedded Information
and Communications Technology (ICT) systems that employ sensor-actuator
networks to offer users with a wide range of smart applications and services
by being aware of their physical surroundings [203]. With the help of
autonomous control loops, many IoT sensors are conceivable because of the
inclusion of improved processing and analysis of data collected by sensors, as
well as planning and executing plans utilizing actuators [204]. Methods are
needed to aid in the design and development of these systems because of their
complexity [205]. In the context of CPS, the systems that are embedded or
software integrated into physical things, networked, and offering residents
and companies with a wide range of smart applications and services are
referred to as ICT systems [206].

Transport systems, buildings, electricity grids, and water infrastructure are
all examples of CPS [207]. This type of CPS is meant to detect and react to
the physical environment, allowing for quick, reliable autonomic control loops
combining sensing and actuation, perhaps with linguistic and cognitive
capabilities, as well [208]. Using wireless sensor/actor networks, CPS can
monitor and respond to the physical environment. Sensors and other alternative
sources collect historical and real-time data, which is used to perform
advanced analysis and processing in the type of autonomic control loops [209].
These loops then plan and execute actions in accordance with a set of goals or
rules. Real-time or historical data is used to support this implementation
[210]. There are several elements that make CPS systems difficult to manage,
including the use of a wide range of sensors and actuators, the necessity for
real-time processing of enormous amounts of data, and the implementation of
plans for issue solving [211]. As a result of the system’s complexity,
engineers need tools and techniques to aid them in the designing process;
adaptive digital twins are poised to play a significant role in de-risking
such complex engineering and cyber-physical projects.

##  5 Explainable AI (XAI) for Next Generation Computing

Intelligent decisions are critical to the success of computing initiatives. Is
a computing system stable and robust enough to execute workloads? Are the
trained models black boxes or causally explainable? These are just a few
examples of challenges that are faced before a computing system can be
implemented [212]. Inaccurate decision-making is expensive in terms of both
cost and resource usage when it comes to these complex and advanced
technologies [213]. There have been a number of AI/ML applications in
computing systems to enhance decision-making for allocation of resources and
energy efficiency. Nevertheless, these AI/ML models’ predictions for computing
systems are still not practical, explainable, or executable [214]. AI/ML
models are frequently hampered by these constraints. Even if QoS continues to
be a primary concern, some recent research have turned their attention to
explaining how QoS is achieved [215]. Is there anything that researchers can
do to further the advancement of the computing community? As a result, a
thorough understanding of Explainable AI (XAI) and hands-on expertise with XAI
tools and approaches [216] is needed in order to make informed resource
management decisions (an example application of AI for Computing). These
issues may be addressed by using Explainable AI approaches, such as
formulating predictions about resource and energy usage and SLA deviations and
then implementing timely, intelligent action to solve them. In order to make
computing more feasible, explainable, and executable, XAI prediction models
must be properly implemented.

##  6 Potential Risks of AI-integrated Computing

AI can save money, but it requires a highly-trained workforce, that can be
expensive at the start. AI’s other drawbacks in computing systems would
include following ones:

  * •

Internet Connectivity Issues: ML/AI techniques based on autonomous computing
are frequently hindered by slow internet connections. There seems to be a
latency between transferring information to the cloud/fog/edge and receiving
replies, even if autonomic computing is faster than traditional computing. ML
methods for servers are prone to this issue since forecasting speed is among
the most important considerations.

  * •

Privacy of Data: AI systems need an enormous quantity of data, which include
information on customers and providers. Understanding who owns the data is far
more beneficial than having private information that can’t be attributed to a
specific individual. Challenges about data security and compliance with
regulations arise frequently when businesses make utilisation confidential
material. Autonomic computing with AI necessitates privacy regulations and
data security.

  * •

Possibility of Errors: Although AI may seem exciting, like with any
experimentation, it isn’t always effective in accomplishing its goals. During
its search for solutions, the AI system generated many problematic statements
on sensitive topics. With AI, there is a high risk of mistakes because to the
many options. Before further usage of this innovation, trust and control must
be established.

  * •

Over-reliance on AI Models: As it stands, AI/ML algorithms are only a small
part of complex software-intensive systems. Software engineers leverage them
for completing highly specialized tasks, while designing an large apparatus of
more traditional algorithms around them, including sensor-data sanitization
and filtering. Further, an AI model is only as good as the data it was trained
on. Hence, strong data engineering processes are required to select
appropriate/representative datasets and conduct various data engineering steps
as well as thorough review processes need to be followed when transfer
learning models are selected. Hence, from an autonomics perspective, all these
steps need to be carefully handled by the MAPE-K loop, which will essentially
need to act both as a software and a data engineer when conducting adaptations
to the ML models.

##  7 Emerging Trends and Future Directions

On the basis of current research, we’ve selected a number of computing study
fields for three distinct maturity levels (5 years, 5 to 10 years, and more
than ten years). We have identified a number of emerging technologies over the
next decade, all of which have the potential to make efficient use of AI/ML-
integrated next-generation computing [122, 217, 218, 219]. Figure 6 depicts
the hype cycle for next-generation computing. There has been a lot of study
done on cloud computing and IoT, but serverless computing is currently at its
peak. Under the umbrella of computing, research fields including fog
computing, edge computing, AI Orchestration, and mobile edge computing are
only getting started. It might take up to ten years for the application of
computing in these fields to reach maturity. However, the hype cycle for
Explainable AI (XAI), AI engineering, hyperscale edge computing, distributed
enterprise, sustainability, quantum Internet and quantum ML is projected to
last more than ten years. The hype around smart robots, digital twins, cyber
security, edge AI, human-centric AI, edge intelligence, dew computing and
privacy enhancing computation is at an all-time high. It is anticipated that
they will be fully developed in less than five years under the umbrella of
autonomous computing. Expected to significantly evolve in the next five to ten
years, MLOps, AIOps, AI-integrated electric vehicles (EV), decision
intelligence and exascale computing have likewise hit their height of
overblown expectations. Generative AI, hyperautomation, neuromorphic
computing, hybrid quantum computing, digital finance, 6G and quantum computing
all have a long way to go before they reach the heights of the hype cycle.
Grid computing and virtualization have received a lot of attention in the past
several years, and they might continue to do so over the next five years.

Figure 6: Hype Cycle for AI-integrated Next Generation Computing

In the following, we have highlighted a number of unsolved problems and
research paths that require further investigation.

###  7.1 Cloud Computing

  * •

New ensemble machine learning approaches for container management systems,
such as Docker Swarm and Kubernetes, are needed to govern user-based QoS-based
container clusters.

  * •

Cloud service dependability and QoS must be maintained through the use of
advanced machine or deep learning techniques.

  * •

Network virtualization must be provided at a reasonable cost in an SDN-based
cloud computing environment that uses AI/ML models to minimise energy
consumption and boost dependability.

  * •

Using AI/ML, cloud-based Big Data analysis tools may find trends in client
behaviour, make better decisions, and better understand their customers. It is
a difficult challenge that has to be solved in order to ensure that scaling
choices are executed or processed in a timely manner utilising AI/ML.

  * •

Thermal-aware task and resource scheduling can be improved by using new AI/ML-
inspired methodologies.

  * •

AI/ML-based autonomic computing is becoming increasingly important as the IoT
and scientific applications grow.

###  7.2 Fog Computing

  * •

It is imperative that the newest AI and ML approaches be used in order to
forecast security vulnerabilities in the fog layer and IoT devices because of
their intrinsic decentralisation.

  * •

AI-based deep learning approaches are needed to estimate resource requirements
in advance for different geographic resources for fog and cloud computing,
which need new policies for provisioning and scheduling resources.

  * •

On diverse fog environments, state-of-the-art AI/ML approaches may be employed
to schedule tasks.

###  7.3 Edge Computing

  * •

Modern computing systems, which incorporate edge devices as component of
datacenters, demand specific IoT-based apps to be created in order to provide
for more encrypted transmission and to protect the privacy of data.

  * •

Due to the resource limitations of IoT edge devices, which can’t run the
robust security software and firewalls built for desktop PCs, Blockchain
technology must be used to enhance security using AI/ML. Moreover, innovative
software architectures such as that facilitate IoT devices patching and
maintenance could be further enhanced by leveraging AI and ML.

  * •

AI/ML-based automated decision-making, rather than human-encoded heuristics,
presents a lucrative path for optimising edge systems with massive volumes of
data through engineering speed and efficiency.

  * •

AI-based big data analytics methods are needed to handle edge device data in
IoT applications at runtime.

###  7.4 Serverless Computing

  * •

AI may be used to enhance the delay and reaction time of tasks in Serverless
computing for IoT applications.

  * •

Automatic heart disease detection in IoT and Serverless computing contexts
requires an ensemble deep learning-based intelligent healthcare system.

  * •

How can deep learning on IoT devices increase real-world performance in AI-
based intelligent systems by leveraging Serverless Computing?

  * •

Serverless systems can benefit from threat mitigation strategies based on
AI/ML, such as clustering model-based security analysis.

###  7.5 Quantum Computing

  * •

To _increase the size_ of current quantum chips, keeping under control the
amount of noise present during the evolution of the quantum states.

  * •

To enter, in full, within the era of Noisy Intermediate-Scale Quantum (NISQ)
devices, which should allow us to simulate the dynamics of _complex_ quantum
systems.

  * •

To integrate quantum chip with quantum error correction (QEC) that will allow
to progress towards Fault-Tolerant Quantum Computation (FTQC). That will allow
to simulate the design and behaviour of novel materials on general purpose
quantum computers, opening up new possibilities in virtually all area of
knowledge, from physics matter to the design of novel AI applications.

  * •

Develop cloud quantum computing infrastructures that, very likely, will be the
way in which we will use quantum computer and simulators: as a booster
supporting our local, classical devices.

  * •

To handle the massive amounts of data created by IoT devices, powerful AI and
reinforcement learning may be used.

  * •

The most recent AI and ML-based methods may be utilised to dynamically
discover and rectify faults to provide a valued and dependable service. Recent
AI and ML approaches can enhance dependability, but they can also raise system
complexity by increasing data processing, which results in higher training
costs for AI and ML techniques as a side effect.

###  7.6 Miscellaneous

The advancements in cloud, fog, and edge in the context of IoT have lead to
concepts such as Cloud-to-Things. The following are interesting research
directions to explore:

  * •

The ability to form Decentralized Autonomous Organizations (DAOs) is a concept
fundamental to the Cloud-to-Things computing continuum.

  * •

Rules and agreements can take the form of Blockchain-enabled smart contracts,
thus providing for trust among the actors and organizing computing across the
continuum [220].

AI/ML can also open up new research directions when used in conjunction with
blockchain technology:

  * •

Smart Oracles can be used to establish decentralised monitoring of vertically
and horizontally distributed heterogeneous infrastructures.

  * •

AI/ML and knowledge management methods can be incorporated both within
functions of Blockchain-enabled smart contracts and Smart Oracles.

##  8 Conclusions and Summary

Computing systems have advanced computer science in the past couple of decades
and are now the heart of the corporate world, providing services based on
Cloud, Fog, Edge, Serverless, and Quantum Computing. Many real-world problems
that require low latency and response time have been solved due to modern
computing systems. This has helped young talents around the globe to launch
start-ups allowing large computing capacity for solving challenging problems
to speed up scientific progress.

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
have become increasingly popular in recent years because of the advances in
accuracy pioneered by them in areas such as computer vision, natural language
processing and other allied applications. For training these models, massive
amounts of data has been collected in the previous few years in addition to
the development in state-of-the-art computing hardware such as the Graphics
Processing Unit (GPU), Google’s Tensor Processing Unit (TPU) and AI Tesla’s
Dojo Processing Unit (DPU). Computational researchers and practitioners should
be aware of AI/ML/DL algorithms and models. With AI/ML/DL, modern computing
may profit from more efficient resource management, while computing is a vital
platform for hosting AI/ML/DL services because of its huge computational
capacity. This means that both sides gain from the other. Large-scale
computing power and external data sources are needed for many AI/ML/DL
techniques, which may be more easily obtained via computing systems. This is
especially important now that methods for training sophisticated AI, ML, and
DL models can be implemented in parallel and in large quantities. To that end,
it is foreseen that continued interest in AI/ML/DL applications will spur new
research into well-established data centre resource management issues
including VM provisioning, consolidation, and load balancing, while also
making it easier to cope with scale-out challenges. Innovative research on
Explainable AI (XAI) might pave the way for more widespread use of AI in