A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud
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Abstract
Due to its flexibility, scalability, and cost-effectiveness of cloud computing, it has emerged as a popular platform for hosting various applications. However, optimizing workflow scheduling in the cloud is still a challenging problem because of the dynamic nature of cloud resources and the diversity of user requirements. In this context, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms have been proposed as effective techniques for improving workflow scheduling in cloud environments. The primary objective of this work is to propose a workflow scheduling algorithm that optimizes the makespan, service cost, and load balance in the cloud. The proposed HGWOCPSO hybrid algorithm employs GWO and Constriction factor based PSO (CPSO) for the workflow optimization. The algorithm is simulated on Workflowsim, where a set of scientific workflows with varying task sizes and inter-task communication requirements are executed on a cloud platform. The simulation results show that the proposed algorithm outperforms existing algorithms in terms of makespan, service cost, and load balance. The employed GWO algorithm mitigates the problem of local optima that is inherent in PSO algorithm.
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Mainak Adhikari , T Amgoth , Satish Narayana Srirama, “A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging Trends”, ACM Computing Surveys 52 (4), pp1-36, 2019.
Kennedy, J. and Eberhart, R.C., "Particle Swarm Optimization", Proceedings of the IEEE International Conference on Neural Networks, 1995.
Mirjalili, S., S.M. Mirjalili, and Lewis, A., "Grey Wolf Optimizer", Advances in Engineering Software, 2014.
A. Maria, Rodriguez, Rajkumar Buyya, “Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds” IEEE Transactions on Cloud Computing, pp. 222-235, 2014.
Chaudhary, A. ., Sharma, A. ., & Gupta, N. . (2023). A Novel Approach to Blockchain and Deep Learning in the field of Steganography. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 104–115. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2514
Chung-Feng Wang, Kui Liu, "A Novel Particle Swarm Optimization Algorithm for Global Optimization", Computational Intelligence and Neuroscience, vol. 2016.
Jian Chang, Zhigang Hu, Yong Tao, Zhou, “Task Scheduling Based on Dynamic Non-Linear PSO in Cloud Environment” IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2018.
Dwarkanath Pande, S. ., & Hasane Ahammad, D. S. . (2022). Cognitive Computing-Based Network Access Control System in Secure Physical Layer. Research Journal of Computer Systems and Engineering, 3(1), 14–20. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/36
Bilal Abed-alguni, Noor Aldeen Alawad, "Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments", Applied Soft Computing, 2021.
Abdullah Alzaqebah, Rizik Al-Sayyed, Raja Masadeh, "Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment", 2nd International Conference on new Trends in Computing Sciences (ICTCS), 2019.
Abdullah Alzaqebah, Rizik Al-Sayyed, Raja Masadeh, "Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment", 2nd International Conference on new Trends in Computing Sciences (ICTCS), 2019.
Jafar Ababneh, "A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem", Journal of Mathematical Problems in Engineering, 2021.
Chirag Chandrashekar, Pradeep Krishnadoss, Vijaya kumar Kedalu Poornachary, Balasundaram Ananthakrishnan, and Kumar Rangasamy , "HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing", Cyber–Physical Systems in Real-Time and Edge Computing for Smart Grids, 2023.
Mwangi, J., Cohen, D., Silva, C., Min-ji, K., & Suzuki, H. Feature Extraction Techniques for Natural Language Processing Tasks. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/137
Pengze Guo, Zhi Xue, "An adaptive PSO-based real-time workflow scheduling algorithm in cloud systems", International Conference on Communication Technology (ICCT), 2017.
D.A.Prathibha, B.Latha, and G. Sumathi, "Efficient scheduling of workflow in cloud enviornment using billing model aware task clustering", Journal of Theoretical and Applied Information Technology, 2014.
Kalka Dubey, S.C. Sharma, “A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing”, Sustainable Computing: Informatics and Systems, Volume 32, 2021, 100605, ISSN 2210-5379.
Shahin Ghasemi, Ali Hanani,"A Cuckoo-based Workflow Scheduling Algorithm to Reduce Cost and Increase Load Balance in the Cloud Environment", International Journal on Informatics Visualization, Vol.3, 2019.
Prof. Virendra Umale. (2020). Design and Analysis of Low Power Dual Edge Triggered Mechanism Flip-Flop Employing Power Gating Methodology. International Journal of New Practices in Management and Engineering, 6(01), 26 - 31. https://doi.org/10.17762/ijnpme.v6i01.53
Prerit Chawda, Partha Sarathi Chakraborty, "An Improved Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing", ", International Journal on Recent and Innovation Trends in Computing and Communication, 2016.
Sriperambuduri Vinay Kumar, M Nagaratna, Lakshmi Harika Marrivada, Task scheduling in cloud computing using PSO algorithm, Smart Intelligent Computing and Applications, Volume 1: Proceedings of Fifth International Conference on Smart Computing and Informatics (SCI 2021), Springer Nature, 2021.
Mei Chen, Machine Learning for Energy Optimization in Smart Grids , Machine Learning Applications Conference Proceedings, Vol 2 2022.
Clerc M., The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation, 1999, Vol. 3, pp. 1951- 1957.
Chen, Weiwei & Deelman, Ewa., “WorkflowSim: A toolkit for simulating scientific workflows in distributed environments”, IEEE 8th International Conference on E-Science, e-Science 2012.
Singh V, Gupta I, Jana PK, "An energy efficient algorithm for workflow scheduling in iaas cloud", J Grid Comput:1–20, 2019.
Gao Y, Zhang S, Zhou J, "A hybrid algorithm for multi-objective scientific workflow scheduling in iaas cloud", IEEE Access 7:125783–125795, 2019.
Dubey K, Shams MY, Sharma S, Alarifi A, Amoon M, Nasr AA, "A management system for servicing multi-organizations on community cloud model in secure cloud environment", 2019, IEEE Access 7:159535–159546.
Xie Y, Zhu Y, Wang Y, Cheng Y, Xu R, Sani AS, Yuan D, Yang Y, "A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud–edge environment. Futur Generation Computer Systems 97:361–378", 2019.