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

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...

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Vydané v:Journal of ambient intelligence and humanized computing Ročník 14; číslo 4; s. 4313 - 4327
Hlavní autori: Pirozmand, Poria, Jalalinejad, Hoda, Hosseinabadi, Ali Asghar Rahmani, Mirkamali, Seyedsaeid, Li, Yingqiu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Springer Nature B.V
Predmet:
Adaptive learning
Algorithms
Artificial Intelligence
Cloud computing
Computational Intelligence
Computer centers
Cost reduction
Criteria
Customer services
Deadlines
Efficiency
Energy consumption
Engineering
Genetic algorithms
Optimization
Original Research
Particle swarm optimization
Quality of service
Resource allocation
Robotics and Automation
Scheduling
Simulation
Task scheduling
User Interfaces and Human Computer Interaction
Improved particle swarm optimization
Metaheuristic
Cloud Computing
Optimization
Task Scheduling
ISSN:1868-5137, 1868-5145
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-023-04541-9