Hybrid Metaheuristic for Virtual Machine Scheduling in Cloud Computing

Authors

  • Sharma R Department of CSE, D.A.V Institute of Engineering and Technology, Jalandhar, India
  • Mann PS Department of IT, D.A.V Institute of Engineering and Technology, Jalandhar, India

DOI:

https://doi.org/10.26438/ijcse/v6i10.734740

Keywords:

CloudComputing, Schedulin, BAT, AntColonyOptimization

Abstract

Cloud Computing is expanding as the next generation platform which would ease the user on pay as you use mode as per requirement. Cloud incorporates a set of virtual machine which comprises equally storage and computational facility. Due to speedy increase in use of Cloud Computing, moving of more and more application on cloud and demand of customers for more services and enhanced results. The fundamental goal of cloud computing is to offer successful access to isolated and geographically circulated resources. Cloud is growing every day and experience many problems such as scheduling. Scheduling means a group of policies to regulate the order of task to be executed by a computer system.VM Scheduling is necessary for efficient operations in distributed environment. This paper combines ant colony optimization and BAT to solve the VM scheduling problem. We discuss and evaluate these techniques in regard of various performance matrices to give an overview of the latest approaches in the field

References

[1] S. Willium, “Network Security and Communication”, IEEE Transaction, Vol.31, Issue.4, pp.123-141, 2012.

[2] Agarwal, A., Siddharth, S., & Bansal, P. “Evolution of cloud computing and related security concerns.”Symposium on Colossal Data Analysis and Networking (CDAN), 2016

[3] Jing Liu, Liang-Jie Zhang, Bo Hu, and Keqing He “CCRA: Cloud Computing Reference Architecture”, IEEE Ninth International Conference on Services Computing,2012

[4] Hoffa C, Mehta G, Freeman T, Deelman E, Keahey K, Berriman B, Good J, “ On the use of cloud computing for scientific workflows”, IEEE Fourth International Conference ,pp. 640-645, 2008.

[5] Er-Dun, Z., Yong-Qiang, Q., Xing-Xing, X., & Yi, C, “ A Data Placement Strategy Based on Genetic Algorithm for Scientific Workflows”,Eighth International Conference on Computational Intelligence and Security,2012.

[6] Wan, C., Wang, C., & Pei, J, “ A QoS-awared scientific workflow scheduling schema in cloud computing”,IEEE International Conference on Information Science and Technology, 2012.

[7] Amal Ganesh, Dr. M.Sandhya, Dr. Sharmila Shankar, “ A Study on Fault Tolerance methods In Cloud Computing”,IEEE International Advance Computing Conference (IACC), 844-849,2014.

[8] Poola D, Garg SK, Buyya R, Yang Y, Ramamohanarao K. “Robust scheduling of scientific workflows with deadline and budget constraints in clouds”, In Advanced Information Networking and Applications (AINA), IEEE 28th International Conference ,pp. 858-865,2014.

[9] Cho, K.-M., Tsai, P.-W., Tsai, C.-W., & Yang, C.-S. “ A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing”, Neural Computing and Applications, 1297–1309,2014.

[10] Puya Ghazizadeh, Ravi Mukkamala, Reza Fathi, “ Modeling and Predicting Fault Tolerance in Vehicular Cloud Computing”, Intl. Conference on Computing and Network Communications , 395-400,2015.

[11] Ding, J., Zhang, Z., T. B. Ma, R., & Yang, Y. “Auction-based cloud service differentiation with service level objectives”, Computer Networks, 231–249, 2015.

[12] Mohammed, B., Kiran, M., Awan, I.-U., & Maiyama, K. M. “An Integrated Virtualized Strategy for Fault Tolerance in Cloud Computing Environment” Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress ,2016.

[13] Chou, L.-D., Chen, H.-F., Tseng, F.-H., Chao, H.-C., & Chang, Y.-J. “ DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization”, IEEE Systems Journal, 1554–1565,2016.

[14] Srimoyee Bhattacherjee1(B), Uttiya Sarkar2, Sunirmal Khatua2,and Sarbani Roy1, “ PMM: A Novel Prediction Based VM Migration Scheme in Cloud Computing”, Springer International Publishing , pp. 107–117, 2017.

[15] Medhat Tawfeek, Ashraf El-Sisi, Arabi Keshk and Fawzy Torkey, “Cloud Task Scheduling Based on Ant Colony Optimization”, The International Arab Journal of Information Technology, Vol. 12, No. 2, March 2015.

[16] X.-S. Yang, “ A New Metaheuristic Bat-Inspired Algorithm”, Nature Inspired Coop-erative Strategies for Optimization (NISCO 2010),Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 ,2010.

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Published

2025-11-17
CITATION
DOI: 10.26438/ijcse/v6i10.734740
Published: 2025-11-17

How to Cite

[1]
R. Sharma and P. S. Mann, “Hybrid Metaheuristic for Virtual Machine Scheduling in Cloud Computing”, Int. J. Comp. Sci. Eng., vol. 6, no. 10, pp. 734–740, Nov. 2025.

Issue

Vol. 6 No. 10 (2018): IJCSE October Edition

Section

Research Article

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