11institutetext: Shenzhen Institute of Advanced Technology, Chinese Academy of
Sciences
Shenzhen, Guangdong, China
<EMAIL_ADDRESS>

Authors’ Instructions

# Resource Management for GPT-based Model Deployed on Clouds: Challenges,
Solutions, and Future Directions

Yongkang Dang  Minxian Xu  Kejiang Ye

###### Abstract

The widespread adoption of the large language model (LLM), e.g. Generative
Pre-trained Transformer (GPT), deployed on cloud computing environment (e.g.
Azure) has led to a huge increased demand for resources. This surge in demand
poses significant challenges to resource management in clouds. This paper aims
to highlight these challenges by first identifying the unique characteristics
of resource management for the GPT-based model. Building upon this
understanding, we analyze the specific challenges faced by resource management
in the context of GPT-based model deployed on clouds, and propose
corresponding potential solutions. To facilitate effective resource
management, we introduce a comprehensive resource management framework and
present resource scheduling algorithms specifically designed for the GPT-based
model. Furthermore, we delve into the future directions for resource
management in the GPT-based model, highlighting potential areas for further
exploration and improvement. Through this study, we aim to provide valuable
insights into resource management for GPT-based models deployed in clouds and
promote their sustainable development for GPT-based models and applications.

###### Keywords:

GPT-based Model, Cloud Computing, Resource Management, Resource Management
Framework, Scheduling Algorithms

##  1 Introduction

The GPT-based model is a language generation model based on the transformer
architecture, which learns the statistical regularities and semantic knowledge
of language through unsupervised pre-training on large-scale text datasets.
The model can then be fine-tuned on specific domain or task data through
supervised or semi-supervised learning to adapt to different language
application scenarios [12] [4]. The GPT-based model can generate natural,
fluent, context-aware, and semantically coherent language content, making it
suitable for applications such as text summarization, machine translation,
sentiment analysis, question answering systems, and chatbots. Figure 1
provides examples of current applications of the GPT-based model for different
areas[11] [3], including academic, medical, office, education [7] and
marketing.

Figure 1: GPT-based Model Application Areas

The GPT-based model was first proposed and developed by OpenAI and has since
been developed into multiple versions and variants, such as GPT-1, GPT-2,
GPT-3, and GPT-4. The main differences between these models lie in the number
of parameters, dataset size, and training methods. For example, GPT-3 is
currently one of the largest GPT-based models, with 175 billion parameters,
requiring 800 GB of storage [12]. It has achieved excellent performance on
multiple natural language processing tasks. Table 1 lists several mainstream
GPT-based models, providing relevant details such as model names, research
teams, release dates, and model sizes, with each row representing a distinct
model.

Table 1: Parameters of Mainstream GPT-based Models [17] Model | Research Teams | Release Date | Size
---|---|---|---
GPT-3 | OpenAI | 28-May-20 | 175B
PanGu-$\alpha$ | Pengcheng Laboratory and other teams | 26-Apr-21 | 207B
OPT-175B | Meta | 3-May-22 | 175B
PaLM |  Google | Apr-22 | 540B
BLOOM | BigScience | Dec-22 | 176B
MT-NLG | Microsoft and NVIDIA | 11-Oct-21 | 530B
Gopher | DeepMind | 8-Dec-21 | 280B
CPM-2 | Beijing Academy of Artificial Intelligence | Jun-21 | 198B
GPT-Neo-X-20B | EleutherAI | Apr-22 | 20B

The rapid development and widespread application of GPT-based model have led
to the increased demand for resources, and the current GPT-based model has
been deployed on public clouds like Azure and Google Cloud for training and
inference, as GPT-based models are typically too large and resource-intensive
to be deployed on edge devices or small-scale hardware. Therefore, they are
better suited for cloud-based deployments, which also makes the resource
management for GPT-based model facing some specific challenges. In order to
summarize these challenges and propose corresponding solutions, in this
section, we will identify the unique characteristics of resource management
for GPT-based models and establish evaluation metrics for this specific
domain.

###  1.1 Unique Characteristics of Resource Management for GPT-based Model

Through extensive research, we identified the following unique characteristics
of resource management for GPT-based model:

Large-scale computational demands due to huge amount of parameters and fine-
tuning: The GPT-based model typically consists of billions of parameters,
necessitating a substantial amount of computational resources during both
training and inference processes [6]. Training GPT models typically requires
specialized hardware such as Graphics Processing Units (GPUs) or Tensor
Processing Units (TPUs) due to the sheer number of calculations involved. In
addition, fine-tuning a pre-trained GPT model on a specific task requires
additional compute resources, as the model needs to adapt to the task through
further training. This complexity makes resource management more intricate,
requiring efficient allocation and utilization of computational resources to
ensure the model operates efficiently.

High storage demands to support rapid data access: The GPT-based model’s large
parameter size requires significant storage space to accommodate model
parameters and intermediate computation results. Running these models can
quickly consume all available memory on conventional hardware. Therefore,
resource management must consider how to effectively manage storage resources
to meet the model’s requirements while ensuring rapid data access and
processing.

High-speed network demands to enable efficient parallelism: During model
training, the GPT-based model handles vast datasets and performs complex
computations and parameter optimization, demanding fast data transmission and
stable network connectivity. In the inference phase, efficient network
resource utilization directly impacts inference speed and response time. The
GPT-based model needs to generate outputs based on inputs and provide results
in real-time or near real-time conditions. Hence, network resources are
crucial for achieving fast responses and efficient inference.

Long training and inference processes than traditional AI models: Traditional
AI applications often have lower computational requirements and faster
inference times. However, due to the complexity and scale of the GPT-based
model, its training and inference processes typically require extended
periods. Resource management must consider how to maintain system stability
and performance over an extended duration while ensuring the rational
allocation and utilization of resources.

Dynamic resource demands from varied complexity of tasks: In practical
applications, the resource requirements of the GPT-based model may vary over
time and across different tasks, e.g. machine translation, text summarization
and question answering. These tasks can have dynamic resources demand due to
the different degree of complexity (e.g. difficulty of questions and expected
output length). Resource management must possess dynamic adjustment
capabilities, allowing for the dynamic allocation of computational and storage
resources based on actual demands to adapt to different stages and tasks.

By understanding and addressing these unique characteristics, effective
resource management strategies can be developed to ensure optimal performance
and utilization of the GPT-based model in various applications.

###  1.2 Evaluation Metrics for Resource Management for GPT-based Model

In order to effectively evaluate the resource management of GPT-based model,
we can consider the following metrics:

Resource Utilization: It refers to the degree to which the model effectively
utilizes available resources during the training or inference process. For the
GPT-based model, resources primarily include computational resources (such as
CPUs and GPUs), storage resources (such as memory and disk space), and network
resources. Evaluating resource utilization involves ensuring that the model
maximizes the use of available resources to improve efficiency and minimize
resource waste. This can be achieved through optimization of scheduling
algorithms and parallel computing techniques. Higher resource utilization
indicates efficient utilization of computational, storage, and network
resources, enhancing overall system performance.

Time Efficiency: It refers to the time taken by the model to complete a set of
given tasks (e.g. makespan metric used in traditional task scheduling). For
the GPT-based models, time efficiency includes both model training time and
inference time. During training, time efficiency focuses on the speed of
parameter updates on a given dataset. Training time efficiency can be improved
through optimization of scheduling algorithms, distributed training, and
hardware acceleration. During inference, time efficiency concerns the speed at
which the model processes input data and generates outputs. Inference time
directly affects the real-time performance and responsiveness of the model in
practical applications. Techniques such as parallel computing, batch
processing, and hardware optimization can improve inference time efficiency.
Higher time efficiency means the model can complete training and inference
tasks more quickly, thereby increasing overall production efficiency.

Cost Efficiency: The cost of the GPT-based model mainly includes computational
cost, storage cost, and network transmission cost. Computational cost
evaluation primarily considers the computational resource expenses required by
the model. This includes model complexity, computational workload, and the
hardware used. Lower computational costs imply relatively lower computational
resource expenses required for specific tasks, reducing resource investments.
Storage cost refers to the expenses associated with storage resources needed
for the model, including model parameters, intermediate results, and cached
data. Storage cost can be measured based on the model’s size and the capacity
of the storage devices used. Lower storage costs indicate that the model
occupies a relatively smaller storage space, reducing storage resource demands
and related expenses. Network transmission cost involves the expenses
associated with network resources for data transmission during the model
training or inference process, including model parameter transmission,
training data transmission, and inference result transmission. Lower network
transmission costs mean the model efficiently utilizes network resources,
reducing data transmission time and bandwidth expenses.

This paper aims to highlight the specific challenges in resource management
for GPT-based models and propose corresponding solutions. The main
contributions of this paper are as follows:

• we summarize the specific challenges in resource management for GPT-based
model and provide a detailed description of these challenges.

• We propose a comprehensive resource management framework for the GPT-based
model that comprises seven different functional components.

• we present three resource management algorithms specifically designed for
the GPT-based model to optimize the resource usage based on different
objectives including resource utilization, load balancing and energy
efficiency.

The rest of this paper is organized as follows: In Section 2, we will
summarize the specific challenges in resource management for GPT-based model.
By highlighting these challenges, we aim to provide directions for
corresponding solutions. To facilitate effective resource management, in
Section 3, we will propose a comprehensive resource management framework for
the GPT-based model. Additionally, in Section 4, we will introduce three
resource management algorithms specifically tailored to the GPT-based model.
Finally, we will conclude this work and discuss several future research
directions for resource management for GPT-based model in Section 5.

##  2 Specific Challenges in Resource Management for GPT-based Model

By identifying the unique characteristics of resource management for GPT-based
model, we summarized the specific challenges in resource management for GPT-
based model deployed on clouds [9] [14] [5] [8] [16] [10]. For a more visual
representation, we presented these specific challenges in Figure 2. We noted
some main challenges such as performance prediction and control, global
manageability, resource heterogeneity, scalable resource management system,
resource pricing strategies, model reliability, model parallelism and data
parallelism, and we will discuss the details in the following sections.

Figure 2: Challenges in Resource Management for GPT-based Model

###  2.1 Performance Prediction and Control

The GPT-based model, constructed based on the Transformer architecture, has a
large parameter size and complex structure. This complexity leads to
significant computational requirements and the need for a large number of
parameters for training and inference. Different tasks typically have varying
complexities, resource demands, and performance expectations for the model.
Even under the same workload and resource configuration, the performance of
the model can be influenced by task characteristics and data properties.
Additionally, different workloads and resource configurations can lead to
variations in resource allocation, data parallelism, and other aspects that
influence performance. These factors make it challenging to predict and
control the behavior and performance of the model under different workloads
and resource configurations.

###  2.2 Global Manageability

Global manageability refers to effectively managing and coordinating
resources, including computational resources, storage resources, and network
resources, in large and complex cloud environments. In the context of GPT-
based model applications, challenges in achieving global manageability
primarily manifest in the following aspects:

Resource scheduling and allocation: Given the massive computational, storage,
and network resource requirements of the GPT-based model, efficient resource
scheduling and allocation algorithms are needed. This includes dynamic
resource allocation across different data centers and geographical locations
to meet user demands and service-level agreements.

Resource monitoring and optimization: Achieving global manageability requires
real-time monitoring of resource usage, performance metrics, and health
status, coupled with automated techniques for resource adjustments and
optimization. Such monitoring mechanisms help maintain efficient resource
utilization, ensure load balancing, and optimize performance bottlenecks,
thereby enhancing overall system performance.

###  2.3 Resource Heterogeneity

Resource heterogeneity refers to the existence of various types or
characteristics of resources within the same system or environment. These
resources can include computational resources (such as CPUs, GPUs, and TPUs),
storage resources (such as disks and solid-state drives), network resources
(such as bandwidth and latency), and others. Resource heterogeneity implies
differences in performance, scale, power consumption, and cost among these
resources. For the GPT-based model, resource heterogeneity poses the following
challenges:

Resource dependencies: Resource dependencies refer to the interdependencies
and associations among different types of resources. In resource management,
it is necessary to consider these dependencies and employ suitable resource
allocation algorithms to optimize the synergistic effects among resources,
thereby improving overall system performance. For example, in the GPT-based
model, the supply of computational resources must match the capacity of
storage resources and network bandwidth to ensure efficient data transmission
and smooth model operation. By fully considering resource dependencies,
resource allocation and utilization can be optimized, maximizing the system’s
potential.

Resource interoperability: Different types of resources are often provided by
different vendors and technologies, necessitating addressing the challenge of
resource interoperability. This involves establishing standards and protocols
to ensure seamless integration and interaction among different types of
resources, improving system compatibility and interoperability. Additionally,
data and model transfer and sharing across different resources need to be
addressed to enable collaborative work across resources.

###  2.4 Scalable Resource Management System

With the development of the GPT-based model, its scale has increased
significantly, demanding huge computational resources. Moreover, as the GPT-
based model is widely applied, data centers face concurrent requests and high
throughput demands. Therefore, a highly scalable computing and storage
infrastructure is required to support model execution and handle massive data.
Furthermore, the resource management system must scale across multiple
dimensions to handle resource management in large-scale data centers. The GPT-
based model requires efficient management and allocation of computational,
storage, and network resources, as well as task scheduling, to meet the
training and inference requirements of the model. Additionally, the resource
management system needs to dynamically allocate and flexibly expand resources
to accommodate different application scenarios with varying scales and
complexities. These requirements pose important challenges to the resource
management system for the GPT-based model.

###  2.5 Resource Pricing Strategies

Resource pricing strategies are crucial for the GPT-based model as they
directly impact resource utilization, user satisfaction, and vendor
profitability. However, several challenges exist in resource pricing
strategies.

Firstly, accurately determining resource costs is a challenge. Resource costs
are influenced by factors such as the vendor, geographical location, and usage
volume. Therefore, a comprehensive consideration of these factors is required
to ensure that resource pricing covers actual costs and attracts user
adoption.

Secondly, balancing the supply-demand relationship is another challenge.
Vendors aim to obtain revenues through resource sales while ensuring stable
supply, while users seek resources at reasonable prices and sufficient support
during peak demand. Therefore, resource pricing strategies need to strike a
balance between supply and demand, meeting user needs while ensuring vendor
profitability.

Additionally, achieving fair resource allocation and pricing is also a
challenge. In multi-user environments, resources must be allocated on-demand
to different users and priced based on usage. Given the variations in user
demands and usage patterns, assuring fair resource allocation and pricing
becomes a complicated problem.

###  2.6 Model Reliability

Due to the complexity of the GPT-based model, such as its large scale and long
training processes, model failures during operation are inevitable. To ensure
model reliability, systems must implement fault detection and fault tolerance
mechanisms to handle resource failures or interruptions promptly. Fault
detection mechanisms proactively identify potential system failures by
monitoring model performance metrics, resource utilization, and other key
parameters. Fault tolerance mechanisms include data backup and recovery
strategies to ensure data integrity and service continuity. Data backup
strategies involve regular backups of the GPT-based model parameters, training
data, and other related data to ensure available backup data for recovery in
case of failures. Recovery strategies ensure quick system recovery and
maintain the continuity of user experience after failures or interruptions.
Through these mechanisms, the system can rapidly detect and respond to faults,
reducing the risks of system downtime and data loss, thus ensuring the
reliability of the GPT-based model.

###  2.7 Model Parallelism and Data Parallelism

In model parallelism, challenges primarily include:

Model partitioning: The GPT-based model typically has a large scale,
consisting of billions or even hundreds of billions of parameters.
Partitioning such a massive GPT-based model into sub-models suitable for
parallel processing is a challenge. Model partitioning needs to consider the
dependencies within the model structure and the communication requirements
among parameters to ensure correctness and efficiency in parallel computation.

Synchronization and communication overhead: Synchronization and communication
are required among sub-models on different devices to ensure the transfer and
aggregation of gradient information during training and enable effective
parameter updates. Synchronization and communication operations can introduce
additional computational and communication overhead, impacting the efficiency
and performance of parallel computation.

Load balancing: Proper distribution of computational load is crucial for model
parallelism to ensure balanced computation across devices. Load imbalance can
result in computational resource waste and decreased efficiency.

In data parallelism, challenges primarily include:

Data partitioning and distribution: The GPT-based model has massive training
data, and partitioning the data into multiple parts and distributing them to
different devices is a challenge. Data partitioning needs to consider data
balance and distribution efficiency to ensure the quality and performance of
parallel training.

Data synchronization and consistency: In data parallel computation, model
synchronization and consistency are crucial to ensure accurate parameter
updates. Efficient data synchronization mechanisms are key to ensuring the
effectiveness of parallel training.

Training speed limitations: In data parallel computation, the training speed
may be limited by the slowest device. If some devices have slower computation
speeds, it will affect the overall training efficiency and speed.

##  3 Resource Management Framework for GPT-based Model

In response to the specific challenges faced by the GPT-based model and based
on the characteristics of resource management for GPT-based model, we propose
a comprehensive resource management framework. This framework aims to
effectively manage critical resources such as computational resources, storage
resources, and network bandwidth required by the GPT-based model, thereby
improving overall model efficiency and ensuring model reliability and service
quality. Figure 3 demonstrates our resource management framework for the GPT-
based model. The resource management framework is divided into several key
components, including Resource Monitor, GPT Task Scheduler, Resource
Allocator, GPT Task Profiler, Synchronizer, QoS Manager, and Resource Adaptor.
The following will provide detailed introductions for each of these
components.

Figure 3: Resource Management Framework for GPT-based Model

Resource Monitor: The Resource Monitor is responsible for real-time monitoring
of computational resources (e.g. CPU, GPU, memory), network resources (e.g.
bandwidth utilization, network latency), and storage resources (e.g. disk) in
the system. It collects and analyzes real-time resource usage and performance
data, providing real-time feedback and reports to support task scheduling and
resource allocation decisions. It also offers visual representations to show
resource usage and performance metric trends.

GPT Task Scheduler: The GPT Task Scheduler handles task scheduling based on
requests from the GPT task queue. It considers task priority, resource
requirements, timeliness, and related constraints to select suitable GPT-based
model instances for task scheduling. By employing appropriate scheduling
algorithms, it determines the execution order of tasks and assigns them to
available GPT model instances.

Resource Allocator: The Resource Allocator dynamically manages system
resources based on task resource requirements, system resource availability,
and load conditions. It employs intelligent resource allocation strategies to
meet the execution needs of tasks. Additionally, the Resource Allocator may
utilize resource prediction and load forecasting models to predict task
resource requirements and system load conditions, enabling more accurate
resource allocation and adjustments.

GPT Task Profiler: The GPT Task Profiler extracts attributes and requirements
of GPT tasks for better understanding and handling. The functionalities of the
task profiler include:

1) Task Attribute Extraction: The GPT Task Profiler extracts task types, input
data features (e.g. text length, language style, domain-specific vocabulary),
and requirements for answering. These attributes contribute to better task
understanding, enabling customized parameter settings for corresponding GPT
model instances and providing essential references for subsequent data
processing and answer generation.

2) Model Resource Requirement Analysis: The GPT Task Profiler analyzes the
resource requirements of GPT tasks, including computational resources and
storage resources. By evaluating the resource requirements of GPT tasks, the
GPT Task Profiler provides accurate foundations for resource allocation and
scheduling.

3) QoS Requirement Extraction: The GPT Task Profiler identifies QoS
requirements of GPT tasks, such as response time, text generation speed, and
answer accuracy. It transfers these requirements to the QoS Manager for
evaluation and management of task QoS requirements.

Synchronizer: The Synchronizer ensures reasonable resource allocation and
smooth execution of GPT tasks by employing distributed consistency protocols.
The Synchronizer includes the following functionalities:

1) Distributed Lock Management: The Synchronizer uses distributed lock
mechanisms to prevent conflicts and competitions for shared resources. This
ensures that only one thread or process can gain exclusive access to resources
at a time.

2) Task State Synchronization: The Synchronizer ensures consistent task states
across different nodes through distributed consistency protocols. This
includes task states such as start, pause, terminate, and completion. By
efficient negotiation and communication, the Synchronizer promptly updates and
shares task state information, enabling other components to accurately
understand task execution progress.

3) Transaction Handling: The Synchronizer employs distributed consistency
protocols for transactional processing of resource management operations. This
means that resource allocation, release, and scheduling operations are atomic
and maintain system consistency. Even in the event of failures or anomalies,
the Synchronizer can perform rollbacks or recovery operations, ensuring the
reliability and correctness of resource management.

4) Fault Recovery: The Synchronizer handles node failures and network
anomalies through distributed consistency protocols. It detects node failures
and takes corresponding measures, such as re-election or reallocation of
resources, to maintain system availability and stability.

QoS Manager: The QoS Manager evaluates and manages the Quality of Service
(QoS) requirements of GPT tasks. The QoS Manager includes the following
functionalities:

1) QoS Requirement Evaluation: The QoS Manager receives GPT task QoS
requirement information from the GPT Task Profiler, such as response time,
processing time, and answer accuracy requirements. It evaluates and quantifies
these requirements to understand different task QoS demands.

2) QoS Optimization Strategy Formulation: Based on the QoS requirements of GPT
tasks and constraints of system resources, the QoS Manager formulates
corresponding optimization strategies. It considers the urgency of GPT tasks
and QoS requirements, combined with system resource availability and
constraints, to provide guidance and suggestions for the GPT Task Scheduler
and Resource Allocator. By adjusting resource allocation priorities,
scheduling algorithm parameters, and other strategies, the QoS Manager ensures
that QoS requirements of GPT tasks are met.

3) QoS Monitoring and Feedback: The QoS Manager monitors the real-time
performance of GPT tasks and QoS metrics. It regularly checks QoS indicators,
such as response time, processing time, and accuracy, and compares them with
GPT task QoS requirements. If the QoS requirements are not met, the QoS
Manager initiates appropriate resource adjustments and optimizations.

Resource Adaptor: The Resource Adaptor is responsible for dynamic resource
scaling based on system load, GPT task demands, and resource usage. It uses
adaptive algorithms and prediction models to automatically adjust resource
allocation for GPT tasks, achieving dynamic resource expansion and
contraction. When the system load or task demands increase, the Resource
Adaptor automatically scales up resources to meet the requirements and
maintain system performance. Conversely, during low system load or reduced
task demands, it timely scales down resources to reduce waste. By optimizing
resource allocation through the Resource Adaptor, resource distribution
becomes more flexible and intelligent, capable of adapting to dynamically
changing resource demands.

##  4 Resource Management Algorithms for GPT-based Model

Based on our resource management framework for GPT-based model, we propose
three resource management algorithms: Maximization of Compute Resource
Utilization, Load Balancing, and Power-efficient optimization, as the resource
utilization, load balancing efficiency and power efficiency are the most
dominant concerns of resource scheduling approaches [15] [13] [2] [1]. These
algorithms aim to allocate available nodes for user requests to achieve
efficient resource management. Through these algorithms, we can maximize the
utilization of computing resources, achieve load balancing, and optimize
energy consumption. The table 2 provides the definitions of variables used in
the algorithms.

Table 2: Variable Definitions Variable |  Definition
---|---
gptRequestQueue |  A queue containing GPT model requests that need to be processed.
availableNodes |  A list of available nodes that can be used to process the GPT model requests.
threshold |  A utilization threshold (0 to 1) that determines the maximum utilization allowed for each resource on a node.
allocationMap |  A dictionary that maps each GPT model request to an allocated node.
request |  The current GPT model request being processed.
node |  The current node being processed.
computeDemand |  The required compute resource for a request.
memoryDemand |  The required memory resource for a request.
storageDemand |  The required storage resource for a request.
computeCapacity |  The compute resource capacity of a node.
memoryCapacity |  The memory resource capacity of a node.
storageCapacity |  The storage resource capacity of a node.
computeDemandPercentage |  The percentage of compute resource requested by the current request, relative to the compute resource capacity of the current node.
memoryDemandPercentage |  The percentage of memory resource requested by the current request, relative to the memory resource capacity of the current node.
storageDemandPercentage |  The percentage of storage resource requested by the current request, relative to the storage resource capacity of the current node.
computeUtilization |  The compute resource utilization of a node.
memoryUtilization |  The memory resource utilization of a node.
storageUtilization |  The storage resource utilization of a node.
allocated |  A flag indicating whether a request has been allocated to a node or not.
minPower |  The minimum power value among all the scanned nodes.

###  4.1 Maximization of Compute Resource Utilization

The goal of algorithm 1 is to optimize the task-to-node assignment by
maximizing the utilization of computing resources. Taking into account the
threshold of node resources, the algorithm prioritizes nodes with high
resource utilization to serve the requests, thereby avoiding resource waste
and maximizing the utilization of node resources. The optimization objective
equation for the algorithm is given in (1).

$\max\frac{\sum\limits_{i=1}^{k}u(N_{i})}{k},$ (1)

where $u(N_{i})$ denotes the compute resource utilization of node $N_{i}$,
$\sum\limits_{i=1}^{k}$ denotes the summation over the compute resource
utilization of each node, and $k$ denotes the number of available nodes.

Algorithm 1 takes as input a queue of GPT model requests, a list of available
nodes, and a threshold value that determines the maximum utilization allowed
for each resource on a node. The algorithm aims to maximize the utilization of
compute resources while ensuring that the utilization of each resource on a
node does not exceed the specified threshold.

To achieve this, the algorithm first estimates the resource demand of each
request in the queue by calling the function request.estimateResourceDemand()
for each request (line 4). It then sorts the queue in descending order of
compute resource demand (line 6) and the available nodes in descending order
of compute utilization (line 7).

For each request in the queue, the algorithm attempts to allocate a node that
can satisfy the request’s resource demands without exceeding the threshold for
any resource. It does this by iterating over each available node and computing
the percentage of compute, memory, and storage resources that would be
consumed by the request if it were allocated to that node (lines 11-14). If
the node’s compute, memory, and storage utilizations, plus the percentage of
resources demanded by the request, are all less than or equal to the
threshold, the node is allocated to the request (lines 15-20).

If no available node can satisfy the request’s resource demands, the algorithm
creates a new node, allocates it to the request, and updates its utilization
accordingly (lines 25-30).

The algorithm returns a dictionary, allocationMap, where each key is a GPT
request and each value is the node allocated to that request.

Algorithm 1 Maximization of Compute Resource Utilization for GPT requests

1:Input: gptRequestQueue, availableNodes, threshold

2:Output: allocationMap

3:for each request in gptRequestQueue do

4: request.estimateResourceDemand() $\triangleright$ Estimate resource demand
for each request

5:end for

6:gptRequestQueue.sortByDescendingComputeResourceDemand() $\triangleright$
Sort requests by descending compute resource demand

7:availableNodes.sortByDescendingComputeUtilization() $\triangleright$ Sort
available nodes by descending compute utilization

8:$allocationMap\leftarrow\\{\\}$

9:for each request in gptRequestQueue do

10: allocated $\leftarrow$ false

11: for each node in availableNodes do

12: computeDemandPercentage $\leftarrow$ request.computeResourceDemand /
node.computeCapacity

13: memoryDemandPercentage $\leftarrow$ request.memoryResourceDemand /
node.memoryCapacity

14: storageDemandPercentage $\leftarrow$ request.storageResourceDemand /
node.storageCapacity

15: if (node.computeUtilization + computeDemandPercentage $\leq$ threshold)
and (node.memoryUtilization + memoryDemandPercentage $\leq$ threshold) and
(node.storageUtilization + storageDemandPercentage $\leq$ threshold) then

16: allocationMap[request] $\leftarrow$ node $\triangleright$ Allocate request
to the current node

17: node.computeUtilization += computeDemandPercentage $\triangleright$ Update
node’s compute utilization

18: node.memoryUtilization += memoryDemandPercentage $\triangleright$ Update
node’s memory utilization

19: node.storageUtilization += storageDemandPercentage $\triangleright$ Update
node’s storage utilization

20: allocated $\leftarrow$ true

21: break

22: end if

23: end for

24: if not allocated then

25: newNode $\leftarrow$ createNewNode() $\triangleright$ Create a new node

26: availableNodes.append(newNode) $\triangleright$ Add the new node to the
list of available nodes

27: allocationMap[request] $\leftarrow$ newNode $\triangleright$ Allocate
request to the new node

28: newNode.computeUtilization += computeDemandPercentage

29: newNode.memoryUtilization += memoryDemandPercentage

30: newNode.storageUtilization += storageDemandPercentage

31: allocated $\leftarrow$ true

32: end if

33:end for

34:return allocationMap $\triangleright$ Return the allocation mapping

###  4.2 Load Balancing

The purpose of algorithm 2 is to optimize the selection of tasks to nodes by
balancing the load across nodes. Considering the thresholds of node resources,
this algorithm prioritizes serving requests from nodes with lower resource
utilization, thereby ensuring load balancing in the system. The optimization
objective equation for the algorithm is given in (2).

$\min\sqrt{\frac{1}{k}\sum_{i=1}^{k}(u(N_{i})-\bar{u})^{2}},$ (2)

where $k$ denotes the number of available nodes, $u(N_{i})$ denotes the
compute resource utilization of node $N_{i}$, and $\bar{u}$ denotes the
average compute resource utilization across all available nodes.

Algorithm 2 is similar to Algorithm 1, but the difference lies in sorting the
list of available nodes in ascending order of compute resource utilization.
This prioritizes the allocation to nodes with lower utilization rates. Other
steps remain the same as Algorithm 1.

Algorithm 2 Load Balancing Algorithm for GPT requests

1:Input: gptRequestQueue, availableNodes, threshold

2:Output: allocationMap

3:for each request in gptRequestQueue do

4: request.estimateResourceDemand() $\triangleright$ Estimate resource demand
for each request

5:end for

6:gptRequestQueue.sortByDescendingComputeResourceDemand() $\triangleright$
Sort requests by descending compute resource demand

7:availableNodes.sortByAscendingComputeUtilization() $\triangleright$ Sort
available nodes by ascending compute utilization

8:$allocationMap\leftarrow\\{\\}$

9:for each request in gptRequestQueue do

10: allocated $\leftarrow$ false

11: for each node in availableNodes do

12: computeDemandPercentage $\leftarrow$ request.computeResourceDemand /
node.computeCapacity

13: memoryDemandPercentage $\leftarrow$ request.memoryResourceDemand /
node.memoryCapacity

14: storageDemandPercentage $\leftarrow$ request.storageResourceDemand /
node.storageCapacity

15: if (node.computeUtilization + computeDemandPercentage $\leq$ threshold)
and (node.memoryUtilization + memoryDemandPercentage $\leq$ threshold) and
(node.storageUtilization + storageDemandPercentage $\leq$ threshold) then

16: allocationMap[request] $\leftarrow$ node $\triangleright$ Allocate request
to the current node

17: node.computeUtilization += computeDemandPercentage $\triangleright$ Update
node’s compute utilization

18: node.memoryUtilization += memoryDemandPercentage $\triangleright$ Update
node’s memory utilization

19: node.storageUtilization += storageDemandPercentage $\triangleright$ Update
node’s storage utilization

20: allocated $\leftarrow$ true

21: break

22: end if

23: end for

24: if not allocated then

25: newNode $\leftarrow$ createNewNode() $\triangleright$ Create a new node

26: availableNodes.append(newNode) $\triangleright$ Add the new node to the
list of available nodes

27: allocationMap[request] $\leftarrow$ newNode $\triangleright$ Allocate
request to the new node

28: newNode.computeUtilization += computeDemandPercentage

29: newNode.memoryUtilization += memoryDemandPercentage

30: newNode.storageUtilization += storageDemandPercentage

31: allocated $\leftarrow$ true

32: end if

33:end for

34:return allocationMap $\triangleright$ Return the allocation mapping

###  4.3 Power-efficient Optimization

The purpose of algorithm 3 is to optimize the selection of tasks to nodes by
minimizing energy consumption, aiming to minimize the total energy consumption
of available nodes. This algorithm prioritizes serving requests on nodes with
the lowest energy consumption, thus achieving power-efficient optimization.
The optimization objective formula for the algorithm is given in (3).

$\text{min }\sum_{i=1}^{k}\text{power}(N_{i}),$ (3)

where $k$ denotes the number of available nodes and $\text{power}(N_{i})$
denotes the power consumption of node $N_{i}$.

Algorithm 3 requires two input parameters: the request queue gptRequestQueue
and the list of available nodes availableNodes (Line 1). The output is the
allocation mapping allocationMap (Line 2).

Firstly, the algorithm creates an empty allocation mapping allocationMap (Line
3), and iterates through each request in the request queue, using the
estimateResourceDemand() function to estimate its resource demand (Line 4-5).
Then, the algorithm sorts the request queue in descending order based on the
resource demand, to handle requests that require more resources first (Line
7). Next, the algorithm iterates through each request in the request queue and
attempts to allocate it to the node with the minimum energy consumption among
the available nodes. For each request, the algorithm iterates through the
available node list and checks if the current node has enough resources to
fulfill the request. If it does, the algorithm calculates the energy
consumption of the node processing that request and compares it with the
current minimum energy consumption. If the energy consumption is less than the
current minimum value, the allocated node and minimum energy consumption are
updated. After iterating through all the nodes for each request, the algorithm
adds the allocated node to the allocation mapping allocationMap (Line 8-23).
Finally, the algorithm returns the allocationMap, which indicates the mapping
of each request to its assigned node (Line 24).

Algorithm 3 Power-efficient Algorithm for GPT requests

1:Input: gptRequestQueue, availableNodes

2:Output: allocationMap

3:allocationMap $\leftarrow\\{\\}$ $\triangleright$ Initialize an empty
mapping of requests to nodes

4:for each request in gptRequestQueue do

5: request.estimateResourceDemand() $\triangleright$ Estimate resource demand
for each request

6:end for

7:gptRequestQueue.sortByDescendingResourceDemand() $\triangleright$ Sort
requests by descending resource demand

8:for each request in gptRequestQueue do

9: minPower $\leftarrow\infty$ $\triangleright$ Initialize minimum power to
positive infinity

10: allocatedNode $\leftarrow\text{NULL}$ $\triangleright$ Initialize the
allocated node to NULL

11: for each node in availableNodes do

12: if node has enough resources for request then $\triangleright$ Check if
the node has enough resources for the request

13: power $\leftarrow$ estimatePower(node, request) $\triangleright$ Estimate
power consumption for allocating request to the node

14: if power $<$ minPower then $\triangleright$ Update the minimum power and
allocated node if a more power-efficient option is found

15: allocatedNode $\leftarrow$ node

16: minPower $\leftarrow$ power

17: end if

18: end if

19: end for

20: if allocatedNode $\neq\text{NULL}$ then $\triangleright$ Allocate the
request to the most power-efficient node

21: allocationMap[request] $\leftarrow$ allocatedNode

22: end if

23:end for

24:return allocationMap $\triangleright$ Return the allocation mapping

##  5 Conclusions and Future Research Directions

In this paper, we first introduced popular GPT-based models, identified the
unique characteristics of resource management for GPT-based model, and
discussed corresponding evaluation metrics. Based on this, we further analyzed
specific challenges in resource management for GPT-based model. To achieve
effective resource management, we also introduced a comprehensive resource
management framework consisting of several key components. Additionally, based
on our resource management framework, we proposed three scheduling algorithms
specifically designed for the GPT-based model for different objectives.
Finally, we explored future research directions for resource management for
GPT-based model , highlighting some potential areas worth investigating. We
hope that research in these areas will draw the attention of researchers and
drive continuous innovation and development in resource management for GPT-
based model .

Although the GPT-based model has developed rapidly and gained widespread
applications in various fields, there is still significant room for
improvement in resource management for GPT-based model. In the future, we need
to explore more efficient and intelligent resource management techniques and
optimization strategies to meet the growing resource demands of the GPT-based
model. We summarize several future research directions for resource management
for GPT-based model as follows:

Specialized Hardware for GPT-based Model: As the scale of the GPT-based model
continues to increase, the computational demands also grow. Future research
will focus on providing higher-performance hardware support for the GPT-based
model. Manufacturers can develop specialized AI chips (such as GPUs, FPGAs,
etc.) optimized for the characteristics of the GPT-based model to meet the
demands of large-scale parallel computations.

Benchmarks for Performance Evaluation: Currently, there is a lack of
standardized benchmarks for resource management for GPT-based model, and
existing evaluation metrics are not comprehensive. Future efforts should
establish more comprehensive test suites to evaluate resource management from
multiple dimensions.

Resource Utilization Maximization: Future research needs to investigate more
efficient resource management techniques to maximize the utilization of
resources by the GPT-based model. Cloud data centers suffer from the issue in
low resource utilization. This can be achieved through dynamic resource
allocation, resource sharing, and parallel computing algorithms designed for
GPT-based models.

Scheduling Algorithms and Metrics: To effectively perform task scheduling and
resource management, future research needs to design specialized scheduling
and optimization algorithms for the GPT-based model. These algorithms should
consider task priorities, resource constraints, and optimization metrics to
achieve better resource allocation and task scheduling strategies.
Additionally, attention should be paid to scheduling algorithm metrics, such
as throughput, response time, task completion rate to evaluate the
effectiveness of the algorithms. Moreover, novel metrics can also be proposed
to measure the metrics suitable for resource provisioning algorithms for GPR-
based model.

Security Management: As the GPT-based model is widely used in various fields
now, security concerns in resource management become increasingly prominent.
Future research directions include: 1) data privacy to ensure effective
protection of user data during the GPT-based model training and inference to
avoid data breaches. 2) model security to prevent malicious attackers from
tampering with the GPT-based model to protect the integrity and reliability of
model parameters and prevent degradation of model performance. 3) system
security to implement security measures to protect computational resources
from malicious attacks, preventing resource abuse, occupation, or damage, and
ensuring the reliability of resource management.

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