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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Poria Pirozmand at Dalian Neusoft University of Information
Poria Pirozmand
Hoda Jalalinejad
Hoda Jalalinejad
Hoda Jalalinejad
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Ali Asghar Rahmani Hosseinabadi at University of Regina
Ali Asghar Rahmani Hosseinabadi
Seyedsaeid Mirkamali at Payame Noor University
Seyedsaeid Mirkamali
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Abstract and Figures

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive Learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria.
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Vol.:(0123456789)
1 3
Journal of Ambient Intelligence and Humanized Computing (2023) 14:4313–4327
https://doi.org/10.1007/s12652-023-04541-9
ORIGINAL RESEARCH
An improved particle swarm optimization algorithm fortask
scheduling incloud computing
PoriaPirozmand1· HodaJalalinejad2· AliAsgharRahmaniHosseinabadi3 · SeyedsaeidMirkamali4· YingqiuLi1
Received: 30 April 2022 / Accepted: 19 January 2023 / Published online: 15 February 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
Abstract
In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling
is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to
identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been
presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimiza-
tion (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle
Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning
strategy is employed. In its initial population phase, the proposed Multi Adaptive Learning for Particle Swarm Optimization
(MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's
variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to
various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the
proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve
the problem in less time and get the best answer for most of the criteria.
Keywords Cloud Computing· Task Scheduling· Metaheuristic· Optimization· Improved particle swarm optimization
1 Introduction
Cloud computing is an evolving technology in the field of
distributed computing and parallel processing. The popular-
ity of cloud computing is increasing due to its unique fea-
tures such as diverse services, security, resilience, and scal-
ability (Mansouri and Javidi 2020). Cloud service providers
provide services such as software, storage space, network
services, etc. to their customers. In order for cloud service
providers to be able to provide such services to their cus-
tomers, they must make the best use of all cloud resources.
This use is effectively done by task scheduling algorithms.
One of the important goals of task scheduling is to increase
performance, Quality of Service (QoS) and also reduce costs
(Bansal and Malik 2020).
Cloud computing is a model for ubiquitously, easily, and
at the user's request, accessing a common set of configurable
computing resources (e.g., networks, data centers, storage
space, applications, and services) through the network that
can be prepared and released quickly with minimal admin-
istrative effort or service provider interaction. The main
purpose of cloud computing is to provide cloud services to
* Ali Asghar Rahmani Hosseinabadi
ark838@uregina.ca; a.r.hosseinabadi1987@gmail.com
Poria Pirozmand
poria@hbu.edu.cn
Hoda Jalalinejad
hoda.jalalinezhad@gmail.com
Seyedsaeid Mirkamali
s.mirkamali@pnu.ac.ir
Yingqiu Li
liyingqiu@neusoft.edu.cn
1 Hebei Key Laboratory ofMachine Learning
andComputational Intelligence, Hebei University,
Baoding071002, China
2 Department ofMathematics andComputer Science, Bandar
Abbas Branch, Islamic Azad University, BandarAbbas, Iran
3 Department ofComputer Science, University ofRegina,
Regina, Canada
4 Department ofComputer Engineering andIT, Payame Noor
University (PNU), Tehran, Iran
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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