Research on Cloud Computing Task Scheduling Based on PSOMC

Authors

  • Kun Li Computer Teaching and Research Section Department of Public Infrastructure, Henan Medical College, Zhengzhou, Henan, 451191, China
  • Liwei Jia Computer Teaching and Research Section Department of Public Infrastructure, Henan Medical College, Zhengzhou, Henan, 451191, China
  • Xiaoming Shi Computer Teaching and Research Section Department of Public Infrastructure, Henan Medical College, Zhengzhou, Henan, 451191, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2161

Keywords:

cloud computing, task scheduling, chaos, adaptive weights

Abstract

How to better reduce the task scheduling time and consumption cost in cloud computing has always been a hot topic of current research. In this paper, we propose a cloud computing task scheduling strategy based on the fusion of Particle Swarm Optimization and Membrane Computing. Firstly, a task scheduling model with time function and cost function as the target is proposed, secondly, on the basis of particle swarm algorithm, chaos operation is used in population initialization to improve the diversity of rich understanding, adaptive weight factor based on sinusoidal function is used to avoid the algorithm falling into local optimum, Membrane Computing is used in individual screening to improve the quality of individual solutions, and finally, in The performance of the PSOMC algorithm is illustrated by comparing six benchmark test functions in simulation experiments, and it is also verified that the completion time and consumption cost are significantly better than those of the ACO, PSO and MC algorithms for different number of tasks.

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Author Biographies

Kun Li, Computer Teaching and Research Section Department of Public Infrastructure, Henan Medical College, Zhengzhou, Henan, 451191, China

Kun Li received the B.S. degree in computer science and technology from Zhengzhou University, Zhengzhou,China, in 2005 and received the M.S. degree in computer software and theory from Zhengzhou University, Zhengzhou, China, in 2008. He is currently a Lecturer Henan Medical College, Zhengzhou, China. His research interests include cloud computing and algorithm design.

Liwei Jia, Computer Teaching and Research Section Department of Public Infrastructure, Henan Medical College, Zhengzhou, Henan, 451191, China

Liwei Jia received the B.S. degree in computer science and technology from Zhengzhou University, Zhengzhou, China, in 2005 and received the M.S. degree in computer software and theory from Zhengzhou University, Zhengzhou, China, in 2008. He is currently a Lecturer Henan Medical College, Zhengzhou, China. His research interests include cloud computing and algorithm design.

Xiaoming Shi, Computer Teaching and Research Section Department of Public Infrastructure, Henan Medical College, Zhengzhou, Henan, 451191, China

Xiaoming Shi received the B.S. degree in computer science and technology from Zhengzhou University Of Light Industry, Zhengzhou, China, in 2005 and received the M.S. degree in computer software and theory from Zhengzhou University, Zhengzhou, China, in 2008. His research interests include algorithm design and multi-agent system.

References

A. Razaque, N.R. Vennapusa, N. Soni, et al., ‘Task scheduling in cloud computing’, 2016 IEEE long island systems, applications and technology conference (LISAT). IEEE, Farmingdale, NY, USA, pp. 1–5. June 2016.

I.M. Ibrahim, ‘Task scheduling algorithms in cloud computing: A review’, Turkish Journal of Computer and Mathematics Education (TURCOMAT), Vol. 12, No. 4, pp. 1041–1053. April 2014.

A.R. Arunarani, D. Manjula, V. Sugumaran, ‘Task scheduling techniques in cloud computing: A Literature survey’, Future Generation Computer Systems, Vol. 91, pp. 407–415. February 2019.

R. Poli, J. Kennedy, T. Blackwell, ‘Particle swarm optimization’, Swarm intelligence, Vol. 1, No. 1, pp. 33–57. August 2007.

T. Bezdan, M. Zivkovic, N. Bacanin N, et al., ‘Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm’, Journal of Intelligent & Fuzzy Systems, Vol. 42, No. 1, pp. 411–423, January 2022.

X. Chen, L. Cheng, C. Liu, et al., ‘A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems’, IEEE Systems Journal. Vol. 14, Issue 3, pp. 3117–3128, January 2020.

S. Nabi, M. Ahmad, M. Ibrahim, et al., ‘AdPSO: adaptive PSO-based task scheduling approach for cloud computing’, Sensors, Vol. 22, No. 3, pp. 920–941, January 2022.

L. Abualigah, M. Alkhrabsheh, ‘Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing’, The Journal of Supercomputing, Vol. 78, No. 1, pp. 740–765, January 2022.

S. Mangalampalli, S.K. Swain, V.K. Mangalampalli, ‘Multi Objective Task Scheduling in Cloud Computing Using Cat Swarm Optimization Algorithm’, Arabian Journal for Science and Engineering, Vol. 47, No. 2, pp. 1821–1830, September 2022.

M. Abd Elaziz, I. Attiya, ‘An improved Henry gas solubility optimization algorithm for task scheduling in cloud computing’, Artificial Intelligence Review, Vol. 54, No. 5, pp. 3599–3637, June 2021.

S. Velliangiri, P. Karthikeyan, V.M.A. Xavier, et al., ‘Hybrid electro search with genetic algorithm for task scheduling in cloud computing’, Ain Shams Engineering Journal, Vol. 12, No. 1, pp. 631–639, March 2021.

L. Abualigah, A. Diabat, ‘A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments’, Cluster Computing, Vol. 24, No. 1, pp. 205–223, March 2021.

N. Manikandan, N. Gobalakrishnan, K. Pradeep, ‘Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment’, Computer Communications, Vol. 187, pp. 35–44, April 2022.

J.H. Ju, W.Z. Bao, Z.Y. Wang, et al., ‘Research for the task scheduling algorithm optimization based on hybrid PSO and ACO for cloud computing’,International Journal of Grid and Distributed Computing, Vol. 7, No. 5, pp. 87–96, May 2014.

M. Agarwal, G.M.S. Srivastava, ‘Genetic algorithm-enabled particle swarm optimization (PSOGA)-based task scheduling in cloud computing environment’,International Journal of Information Technology & Decision Making, Vol. 17, No. 4, pp. 1237–1267, May 2018.

F. Hemasian-Etefagh, F. Safi-Esfahani, ‘Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing’, The Journal of Supercomputing, Vol. 75, No. 10, pp. 6386-6450, October 2019.

G. Pãun, ‘A quick introduction to membrane computing’, The Journal of Logic and Algebraic Programming, Vol. 79, No. 6, pp. 291–294, August 2010.

Published

2022-11-09

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