research-article

An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud Computing

Authors:

Haifeng LuAuthors Info & Claims

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

Article No.: 142, Pages 1 - 5

https://doi.org/10.1145/3207677.3278089

Published: 22 October 2018 Publication History

Abstract

Task scheduling1 is a very important part of the cloud computing environment. Aiming at the characteristics of task scheduling and considering both users and cloud service providers, this paper proposes an improved particle swarm optimization algorithm based on adaptive weights. The algorithm uses adaptive weights to make the weight change with the increase of the number of iterations, and introduces random weights in the later stage, which avoids the situation that the particle swarm algorithm may be trapped in the local optimum when it comes to late stage. Applying the algorithm to task scheduling in cloud computing can achieve a better scheduling plan. The experiment results show that under the same conditions, the improved particle swarm optimization algorithm is better than the standard particle swarm optimization algorithm, which improves the using efficiency of resource while ensuring the task completion time.

References

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Digital Library

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Zhan S and Huo H. 2012. Improved PSO-based task scheduling algorithm in cloud computing. J Inform Comput Sci. 9:3821--3829.

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Negar Dordaie and Nima Jafari Navimipour. 2017. A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments. ICT Express.

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Divya Chaudhary and Bijendra Kumar. 2017.A New Balanced Particle Swarm Optimisation for Load Scheduling in Cloud Computing. Journal of Information & Knowledge Management, 17.

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Cited By

Zhang YZhang SZhang QFan J(2025)An intention-driven task offloading strategy based on imitation learning in pervasive edge computingComputer Networks10.1016/j.comnet.2024.110998257(110998)Online publication date: Feb-2025
https://doi.org/10.1016/j.comnet.2024.110998
Khan ZAziz IOsman NUllah I(2023)A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future DirectionsIEEE Access10.1109/ACCESS.2023.334387711(143417-143445)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3343877
Syed DIslam NShabbir MManzar S(2022)Applications of Big Data in Smart Health SystemsHandbook of Research on Mathematical Modeling for Smart Healthcare Systems10.4018/978-1-6684-4580-8.ch004(52-85)Online publication date: 24-Jun-2022
https://doi.org/10.4018/978-1-6684-4580-8.ch004
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Index Terms

An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud Computing
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Theory of randomized search heuristics

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CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

October 2018

1083 pages

ISBN:9781450365123

DOI:10.1145/3207677

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zhihong Qian

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2018

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CSAE '18

CSAE '18: The 2nd International Conference on Computer Science and Application Engineering

October 22 - 24, 2018

Hohhot, China

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CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;

Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Zhang YZhang SZhang QFan J(2025)An intention-driven task offloading strategy based on imitation learning in pervasive edge computingComputer Networks10.1016/j.comnet.2024.110998257(110998)Online publication date: Feb-2025
https://doi.org/10.1016/j.comnet.2024.110998
Khan ZAziz IOsman NUllah I(2023)A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future DirectionsIEEE Access10.1109/ACCESS.2023.334387711(143417-143445)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3343877
Syed DIslam NShabbir MManzar S(2022)Applications of Big Data in Smart Health SystemsHandbook of Research on Mathematical Modeling for Smart Healthcare Systems10.4018/978-1-6684-4580-8.ch004(52-85)Online publication date: 24-Jun-2022
https://doi.org/10.4018/978-1-6684-4580-8.ch004
Kruekaew BKimpan W(2022)Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement LearningIEEE Access10.1109/ACCESS.2022.314995510(17803-17818)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3149955
Hamdani MAklouf Y(2021)Enhanced active VM load balancing algorithm using fuzzy logic and K-means clusteringMultiagent and Grid Systems10.3233/MGS-21034317:1(59-82)Online publication date: 19-Apr-2021
https://doi.org/10.3233/MGS-210343
Zhao FZhou XXie XWang K(2021)Design of Gradient Magnetic Field Coil Based on an Improved Particle Swarm Optimization Algorithm for Magnetocardiography SystemsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2021.310667770(1-9)Online publication date: 2021
https://doi.org/10.1109/TIM.2021.3106677
Jia ZPan LLiu XLi X(2021)A novel cloud workflow scheduling algorithm based on stable matching game theoryThe Journal of Supercomputing10.1007/s11227-021-03742-3Online publication date: 27-Mar-2021
https://doi.org/10.1007/s11227-021-03742-3
Abualigah LDiabat A(2020)A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environmentsCluster Computing10.1007/s10586-020-03075-5Online publication date: 12-Mar-2020
https://doi.org/10.1007/s10586-020-03075-5
Rjoub GBentahar JAbdel Wahab OSaleh Bataineh A(2020)Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systemsConcurrency and Computation: Practice and Experience10.1002/cpe.591933:23Online publication date: 27-Jul-2020
https://doi.org/10.1002/cpe.5919
Chen Wxu h(2019)Cloud Computing Task Scheduling Method Based on a Coral Reefs Optimization Algorithm2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS47876.2019.00013(27-34)Online publication date: Dec-2019
https://doi.org/10.1109/ICPADS47876.2019.00013
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