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Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach

Fog-cloud computing is a promising approach to enhance distributed systems’ efficiency and performance. Though, managing resources and balancing workloads in such environments remains challenging due to their inherent complexity and dynamic nature. The need for effective load-balancing techniques in...

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Main Authors: Al-Hashimi, Mustafa, Rahiman, Amir Rizaan, Muhammed, Abdullah, Hamid, Nor Asilah Wati
Format: Article
Published: Little Lion Scientific 2023
Online Access:http://psasir.upm.edu.my/id/eprint/110240/
https://www.jatit.org/volumes/hundredone18.php
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spelling my.upm.eprints.1102402024-09-03T07:25:25Z http://psasir.upm.edu.my/id/eprint/110240/ Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach Al-Hashimi, Mustafa Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati Fog-cloud computing is a promising approach to enhance distributed systems’ efficiency and performance. Though, managing resources and balancing workloads in such environments remains challenging due to their inherent complexity and dynamic nature. The need for effective load-balancing techniques in fog-cloud computing systems is crucial to optimize resource allocation, minimize delays, and maximize throughput. This article presents a reinforcement learning (RL)-based load balancing system for fog-cloud computing, employing two RL agents: one for allocating new tasks to fog or cloud nodes and another for migrating tasks between fog nodes to ensure fair distribution and increased throughput. This study derived up with novel state, action, and reward models for both agents, facilitating collaboration during the load-balancing process. Three types of rewards for the RL agents are explored: single objective, multi-objective under non-dominated sorting, and multi-objective under lexicographical sorting. The performance of these methods is assessed using metrics such as average utilization, number of tasks completed, serve rate, and delay. The experimental results showed that RL-based scheduling methods, particularly the Reinforce Learning Multiple Objective (RLRLM) with RL-based migration method outperforms greedy on CPU (GRc) and greedy on reliability (GRr) methods across all performance metrics. The choice of migration method and reward type also influences performance. These finding highlight RL’s potential in optimizing fog-cloud computing and offer valuable insights for future research and practical applications in this field. Little Lion Scientific 2023 Article PeerReviewed Al-Hashimi, Mustafa and Rahiman, Amir Rizaan and Muhammed, Abdullah and Hamid, Nor Asilah Wati (2023) Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach. Journal of Theoretical and Applied Information Technology, 101 (18). 7228 - 7237. ISSN 1992-8645; ESSN: 1817-3195 https://www.jatit.org/volumes/hundredone18.php
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Fog-cloud computing is a promising approach to enhance distributed systems’ efficiency and performance. Though, managing resources and balancing workloads in such environments remains challenging due to their inherent complexity and dynamic nature. The need for effective load-balancing techniques in fog-cloud computing systems is crucial to optimize resource allocation, minimize delays, and maximize throughput. This article presents a reinforcement learning (RL)-based load balancing system for fog-cloud computing, employing two RL agents: one for allocating new tasks to fog or cloud nodes and another for migrating tasks between fog nodes to ensure fair distribution and increased throughput. This study derived up with novel state, action, and reward models for both agents, facilitating collaboration during the load-balancing process. Three types of rewards for the RL agents are explored: single objective, multi-objective under non-dominated sorting, and multi-objective under lexicographical sorting. The performance of these methods is assessed using metrics such as average utilization, number of tasks completed, serve rate, and delay. The experimental results showed that RL-based scheduling methods, particularly the Reinforce Learning Multiple Objective (RLRLM) with RL-based migration method outperforms greedy on CPU (GRc) and greedy on reliability (GRr) methods across all performance metrics. The choice of migration method and reward type also influences performance. These finding highlight RL’s potential in optimizing fog-cloud computing and offer valuable insights for future research and practical applications in this field.
format Article
author Al-Hashimi, Mustafa
Rahiman, Amir Rizaan
Muhammed, Abdullah
Hamid, Nor Asilah Wati
spellingShingle Al-Hashimi, Mustafa
Rahiman, Amir Rizaan
Muhammed, Abdullah
Hamid, Nor Asilah Wati
Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
author_facet Al-Hashimi, Mustafa
Rahiman, Amir Rizaan
Muhammed, Abdullah
Hamid, Nor Asilah Wati
author_sort Al-Hashimi, Mustafa
title Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
title_short Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
title_full Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
title_fullStr Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
title_full_unstemmed Reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
title_sort reinforcement learning based load balancing for fog-cloud computing systems: an optimization approach
publisher Little Lion Scientific
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/110240/
https://www.jatit.org/volumes/hundredone18.php
_version_ 1811686061674332160
score 13.211869

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