# Efficient Resource Management in Cloud Environment

Smruti Rekha Swaina, Ashutosh Kumar Singha, Chung Nan Leeb
aDepartment of Computer Applications, National Institute of Technology,
Kurukshetra, Haryana-136119, India
bDepartment of Computer Science and Engineering, National Sun Yat-sen
University,
Kaohsiung, 804201, Taiwan

Abstract.

In cloud computing resource management plays a significant role in data
centres and it is directly dependent on the application workload. Various
services such as Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), and Software as a Service (SaaS) are offered by cloud computing to
provide compute, network, and storage capabilities to the cloud users
utilizing the pay-per-usage approach. Resource allocation is a prior solution
to address various demanding situations like the under/overload handling,
resource wastage, load balancing, Quality-of-Services (QoS) violations, VM
migration and many more. The primary aim of Virtual Machine Placement (VMP) is
mapping of Virtual Machines (VMs) to physical machines (PMs), such that the
PMs may be utilized to their maximum efficiency, where the already active VMs
are not to be interrupted. It provides a list of live VM migrations that must
be accomplished to get the optimum solution and reduces energy consumption to
a larger extent. The inefficient VMP leads to wastage of resources, excessive
energy consumption and also increase overall operational cost of the data
center. On this context, this article provides an extensive survey of resource
management schemes in cloud environment. A conceptual scheme for resource
management, grouping of current machine learning based resource allocation
strategies, and fundamental problems of ineffective distribution of physical
resources are analyzed. Thereafter, a complete survey of existing techniques
in machine learning based mechanisms in the field of cloud resource management
are explained. Ultimately, the paper explores and concludes distinct
approaching challenges and future research guidelines associated to resource
management in cloud environment.

_Keywords_ Virtual Machines, Physical Machines, Virtual Machine Placement,
Resource Management, VM Migration

##  1 Introduction

Cloud computing (CC) is an imminent distributed computing model that offers
on-demand and low-cost shared computing resources to users [1]-[5]. It is an
appropriate platform for large amount of data processing due to its on-demand
elasticity or flexibility, incredibly low-latency and highly scalable
processing capabilities[6][7]. CC is an inevitable distributed computing model
that offers on-demand and low-cost shared computing resources to users[8][9].
It is an appropriate platform for a large amount of data processing due to its
on-demand elasticity or flexibility, extremely low latency, and massively
parallel processing features [10]. To deal with the ever growing and dynamic
requests of cloud clients, it is necessary to achieve the Quality of Service
(QoS) level among cloud clients and their cloud service providers (CSP) [11].
The CSPs must ensure proper synchronization among resource and power
management within the data center while maintaining the required QoS for
hosted applications. The efficiency of a data center substantially depends
upon how VMs are sustained and where they are placed [12][13]. The process of
selecting the most appropriate physical machine (PM) in large cloud data
centers is termed as VMP mechanism. The high power consumption inside the data
center is due to the low utilization of computing resources such as CPU,
memory, etc [14]-[16]. In the idle state, the server machines consume about
60%-70% power of their maximum power. High-energy consumption leads to an
increase in the overall operational cost of the data center[17]-[20]. CC
models are classified into two categories, i.e., the service model and the
deployment model. Organizations choose a specific model based on their
requirement. Fig. 1 shows the conceptual resource management framework in a
cloud environment.

Figure 1: Conceptual Resource Management Framework in a Cloud Environment

The process of allocating various resources, i.e., memory, storage, and
bandwidth, to a set of applications is termed resource management[21]. The
Cloud Service Providers (CSPs) must ensure efficient and effective resource
utilization by following the Service Level Agreements (SLAs) with the cloud
users. Cloud data centers use virtualization technology in order to get
minimum energy usage and maximum resource usage. Virtualization allows to
sharing of the resources of the same PM among various VMs [22]. Depending upon
the incoming demands list of VMs can be dynamically allocated and de-allocated
in a single PM. Each VM has its own characteristics and consumes a distinct
amount of power based on resource usage. Server consolidation approaches are
used to consolidate the workloads on a fewer number of machines, and unused
resources can be switched to a low-power mode or can be turned off to save
energy [23]. Efficient VM mechanisms assign VMs to PMs in such a way as to
increase resource usage, which reduces the number of active PMs. Hence,
minimum energy consumption is achieved. Fig 2. shows the details of cloud
computing models.

Figure 2:  Cloud Computing Models

Resource management of cloud data centers is the major challenge which can be
classified into three different levels, i.e., workload management at the
application level, virtual machine level, and physical machine levels, as
shown in Fig. 3. At the application level, during task scheduling and task
assignment operations, various management techniques are required. Task
assignment refers to the selection of appropriate VMs for task execution, and
task scheduling deals with the selection of a task for its execution. Task
scheduling and task assignment can be achieved by using various mechanisms, as
shown in Fig. 3. At the virtual machine level, workload management can be
divided into three categories, i.e., VM assignment, VM consolidation, and VM
assignment. The process of selecting the most appropriate PMs for VM is
referred as VM assignment or VM placement. VM migration is referred as moving
VMs from over/under-loaded PMs to a selected optimal PM, and VM consolidation
is a technique to place VMs on the minimum number of PMs in order to reduce
energy consumption and improve resource utilization. Various VM placement and
VM migration approaches are shown in Fig. 3. Server consolidation, over/under-
load handling at the server, and resource provisioning are the primary
operations associated with resource management at the physical level. The
server consolidation is a technique that minimizes the number of active PMs by
using power-efficient VMP approaches. The over/under-load handling deals with
the identification of respective conditions at the server and mitigates its
impact by using VM migration. The efficient distribution of physical resources
among VMs before the arrival of the actual workload from cloud users is
referred as resource provisioning. Different under/over-load handling
mechanisms and resource provisioning approaches are illustrated in Fig. 3.

Figure 3:  Resource Management Techniques

##  2 Motivation

To deal with the ever-growing and dynamic demands of cloud clients, a huge
number of VMs are placed on more PMs. The VMP performs a crucial role in power
consumption, resource utilization, and the overall operational cost of the
data center. High power consumption within the data center is due to
inefficient use of these resources. Excess energy consumption by cloud data
centers facilities high operational costs and, in turn, increases carbon
dioxide ($CO_{2}$) levels indirectly. It has been noted that cooling systems
and PMs collectively use approximately 70% of the overall power used in a data
center [24][18]. Power consumption, resource utilization and carbon foot print
are fundamental components for CSPs to control operational costs. Due to
unbalanced VM placement, most of the time, it has been observed that resources
are either underutilized or overloaded, which leads to wastage of resources
and compromises the quality of service parameters. The server machines consume
about 60%-70% power of their maximum power in the idle state [25][20]. Server
consolidation mechanisms are used to consolidate the workloads on a lesser
number of machines, and unused resources can be switched to a low-power mode,
e.g., using DVFS [26][22] or can be turned off to save energy. Various energy-
saving approaches reduce the performance of the system and create a complex
balance between power savings and high performance. It shows the overload and
under-utilization of resources against the typical resource capacity of a data
center. Some of the key challenges due to inefficient distribution of physical
resources at the data center are excess power consumption, resource wastage,
performance degradation, frequent VM migration, increased SLA violations, and
minimized QoS. To overcome these issues, the data centers must have ideal
resource constraint capacity in order to satisfy the incoming loads without
wasting resources. Again, the resource manager can achieve this if the
accurate forecasting of future demand is known beforehand. This emphasizes the
requirement for automatic resource management strategies that allows systems
to auto-adapt based on variable resource demands by efficiently using the
current resources.

##  3 Related Work

Multi-objective energy efficient VMP
Sharma et al. [34] proposed an energy-efficient multi-objective VMP framework
to minimize power consumption, maximize resource utilization and reduce SLA
violation. The integration of genetic algorithm (GA) and particle swarm
optimization (PSO) termed the HGAPSO algorithm was used to assign VMs on PMs.
The migration of VMs from inefficient to the optimal PM is accomplished by GA
however, it has poor convergence. So, PSO assists GA in choosing the most
optimal PM within data center. The drawback of this approach is it is not
applicable in a heterogeneous environment.

Secure and Energy Aware Load Balancing (SEA-LB)
Singh et al. [35] suggested a secure and energy aware load balancing (SEA-LB)
scheme for VMP problem. This approach use GA to improve resource utilization,
minimize power consumption and decreasing number of conflicting server by
providing security to each VM within the data center during VMP. The lack of
this scheme is that it generally leads to untimely convergence.

Secure and Multi-objective VM Placement
Saxena et al. [30] introduced a Secure and Multi-objective Virtual Machine
Placement (SM-VMP) mechanism based on NSGA-II and whale optimization
evolutionary technique. This work focused on reduction in security threats,
energy consumption, communication cost and enhanced resource utilization
simultaneously. A probability based encoding and decoding scheme was suggested
to encode VM allocations as a whale position vector. To check the efficacy of
this approach, the obtained outcomes are compared with various existing multi-
objective VMPs such as HGAPSO, Non-dominated based GA (NSGA-II), Random-fit,
First-fit and Best-fit.

Multi-objective Load Balancing
Saxena et al.[33] introduced a multi-objective load-balancing (OP-MLB)
technique. It is a power-efficient and online resource prediction based
approach. Evolutionary neural-network used to predict future demands of VMs.
Proactive under/over-load identification on PMs is carried out and NSGA-II
algorithm is used for VM placement. An energy and network efficient VM
migration technique was used to reduce power consumption within the data
center. It permits energy saving by switching off the idle PMs and decreasing
the number of active PMs, minimizing VM migrations and improving the resource
utilization.

Multi-input and Multi-output Neural Network
Saxena et al. [32] proposed a multi-resource feed-forward evolutionary neural
network based prediction technique by applying modifications of an existing
single input and single output (SISO) feed-forward neural network. To collect
multiple inputs and predict output based on multiple attributes there are sets
of neural nodes in stead of conventional nodes at input, hidden and output
layers. This approach enhanced prediction accuracy with nominal requirement of
time complexity and space complexity as compared to SISO neural networks. This
incorporated technique optimized resource utilization and energy consumption
of the data center.

Quantum Neural Network
Singh et al. [36] suggested a resource prediction model for cloud environment.
This work uses Evolutionary Quantum Neural Network (EQNN) which is the
combination of precise concepts of quantum computing and evolutionary
algorithm. Quantum gates such as rotation gates and C-NOT gates are used as an
activation function inside the neural network. During training phase, there is
a assumption that qubits have superior exploration and exploitation capacity
than actual numbers. Qubits are generated from pre-processed training input
data with the help of rotation gate. Then, qubit values are generated for
network connections instead of real-numbered weights.

Evolutionary Neural Network
A workload prediction model suggested by Kumar et al.[29] for cloud
environment by applying neural network and self adaptive differential
evolution (SaDE) algorithm. This approach deals with collection of historical
data from various cloud users then pre-processing over these data is conducted
by normalizing it in the range of [0, 1]. Here, prediction model predict
future workload on data center at n + 1 time instance by extracting patterns
from actual workload and analysing of n previous workload values. Then, SaDE
algorithm is applied to train the neural network. The more accurate results
are obtained due to fast convergence, higher exploration, and learning
capability of the SaDE algorithm.

Kumar et al. [28] proposed a workload prediction approach that is based on
neural network model with supervised learning mechanism. A biphase adaptive
differential evolution (BaDE) learning algorithm used to train the neural
network which motivated by dual adaptation such as, at crossover level during
exploitation process and mutation in exploration phase to enhance the
performance of neural network. This technique provided better prediction
accuracy.

Long Short-term Recurrent Neural Network (LSTM-RNN)
Long short-term memory model in a recurrent neural network (LSTM-RNN) proposed
by Kumar et al.[27] for load prediction of fine-grained host. This model use
the Back-propagation algorithm between recurrent layers and obtains better
prediction accuracy for server loads. The major drawback of this approach is
that, during the training phase it takes long computational time.

Communication Cost Aware Resource Efficient Load Balancing
Saxena et al. [31] proposed a communication cost aware and resource efficient
load balancing (CARELB) scheme to minimize communication cost among VMs,
reduce power consumption and improve resource utilization of the data center.
VMs are assigned with high affinity and inter-dependency physically nearer to
each other. In order to obtain optimal VM allocations a combined concept of
Particle Swarm Optimization and NSGA-II i.e. PSOGA algorithm was used.

Complex-Valued Neural Network
Qazi et al. [37] proposed a multi-layered neural networks with multi-valued
neurons (MLMVN) prediction model. A complex-valued neural network with a
derivative-free feed-forward learning was used by this approach. Since,
complex-valued neural network is more efficient than traditional real-valued
based neural network. It allows high learning capability and better accuracy
in minimum time.

Multi objective VM placement
A workload prediction and allocation technique suggested in [38]. It is a
multi-objective allocation approach that deal with memory and CPU utilization
of VMs and PMs ensures minimum power usage within the data center. The GA was
used to predict future resource usage before assigning VMs to PMs. After that
VMP algorithm was employed to enhance resource utilization and minimize power
consumption.

Table 1:  Comparative Summary Author, Objective |  Approach |  Strength |  Weakness
---|---|---|---
Sharma et al., 2016 [34], Multi-objective VM placement |  Hybrid of PSO and GA algorithm |  Maximizes resource utilization and minimizes power consumption |  Resource wastage
Singh et al., 2019 [35], Multi-objective VM placement |  NSGA-II |  Consideration of security during load balancing |  VM migration is not considered
Saxena et al., 2021 [30], Multi-objective VM placement |  Whale Optimization Algorithm and NSGA-II |  Maximize resource utilization, minimize energy consumption, communication cost and security |  Cluster level VM mapping is not considered
Saxena et al., 2021 [33], online predictive resource management |  multi-objective VM placement based on Online VM resource-usage using evolutionary optimization mechanisms |  Energy-efficient VM placement with proactive handling of overload |  inaccurate overload prediction may lead to unnecessary VM migration
Saxena et al., 2021 [32], VM auto scaling |  Workload prediction and clustering technique is used to get accurate number and size of VMs |  Energy efficient VM placement |  under-loaded servers are ignored
Singh et al. ,2021 [36], workload prediction model |  Quantum based evolutionary neural network optimized by Self-balanced adaptive differential evolutionary algorithm |  Higher and stable prediction accuracy |  Higher complexity because of generation and processing of qubits
Kumar et al., 2018 [29], workload prediction model |  Artificial Neural Network trained with SaDE evolutionary algorithm |  enhanced prediction accuracy over Back-propagation algorithm |  Static approach for prediction
Kumar et al., 2018 [27], workload prediction model |  Long short-term memory model in a recurrent neural network (LSTM-RNN) |  better accuracy than ANN |  Long Computational time
kumar et al., 2020 [28] |  Bi-phase adaptive differential evolution algorithm used to optimize neural network |  Faster Convergence |  Higher complexity than SaDE algorithm optimized neural network
Saxena et al., 2020 [31], Communication-cost aware VM placement |  Integration of PSO and NSGA-II algorithm (PSOGA) |  enhances resource utilization and minimizes response-time |  VM mapping at cluster-level is not considered

##  4 Emerging Challenges and Future Research Directions

In the light of current resource management mechanisms, the subsequent issues
are summarized for the improvement and advancement of more promising resource
management framework in cloud environment:

  1. 1.

Enhancing accuracy of Resource prediction Model : In order to avoid SLA
violations and permit contention free execution of user task, it is necessary
to improve the VM resource prediction accuracy. The accurate prediction of
future resource requirement provides reliable and highly available services to
cloud users. Most of the existing techniques are static and deals with ancient
workload only.

  2. 2.

Overloading and overall Performance Degradation: In overload condition, the
collective resource demand of VMs exceeds the available resource capacity of
the PMs leading to overloaded PMs. It results overall performance degradation
and SLA violations such as, unavailability of resources, longer response time,
some VMs may crash etc.

  3. 3.

Live migration of VMs and Downtime: To handle the variable user demands and
arbitrary requirement of excess resource capacities or to handle overload
situations, VMs are switched from overloaded PMs to most optimal PM which has
ample resource capacity that leads to delayed execution. Another primary
challenge is to decide which VM should migrate, when to migrate, and to which
PM it should migrate?

  4. 4.

Lack of security during resource allocation: Security is the major concern of
every cloud user. Resource management policies must ensure the distribution of
physical resources among various users’ VMs must consider the security of data
as a principal perspective along with capacity constraint of resources.

  5. 5.

Consideration of single resource during load balancing: Most of the current
elastic resource distribution strategies deals with single resource such as
CPU only, whereas all the resources are significant as contention of either of
the resources i.e., memory, bandwidth, disk usage, processor may become
bottleneck degrading overall performance of the system in real world cloud
resource management.

  6. 6.

Interaction and Co-operation among associated operations: Separate
consideration ofresource prediction, resource provisioning, VM placement and
VM migration, nevertheless all of these operations provides common objective
of energy-efficient resource utilization. Therefore, all the related resource
management operations must be employed at homogeneous platform to permit
interaction among them for advancement of a realistic resource management
solution in cloud environment.

The subsequent research guidelines are designed which will fill the research
gaps and deal with aforesaid challenges:

  1. 1.

An enhanced resource prediction technique with maximum accuracy is required to
be developed. The resource predictor should analyze dynamically in real-time
by training and re-training itself with ancient data along with real-time
resource usage by distinct VMs and PMs. Additionally, multiple resources must
be expected simultaneously by analyzing utilization of various resources from
the training data samples.

  2. 2.

An efficient over/under-load forecasting technique is necessary which can
primitively compute the high and down load situation with maximum accuracy
that can allow migration of VMs before occurrence of the under/over-load and
reduce performance degradation.

  3. 3.

To optimize multiple resources usage such as memory, CPU, bandwidth
simultaneously throughout load balancing for contention-free execution of
users’ task while preserving power, decreasing resource wastage, minimizing
carbon emission in cloud data center.

  4. 4.

To develop a secure resource distribution schemes for cloud environment which
permits secure execution of users task, secure inter communication between
interdependent VMs.

  5. 5.

To develop a reliable fault tolerant framework to enhance the resource
utilization and meet QoS parameter.

  6. 6.

To incorporate workload prediction technique with an efficient load balancing
approach at systematic platform to improve the effectiveness during resource
management.

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