Performance Evaluation of Various VM Migration based Nature-inspired Mechanisms in Cloud Environment
Main Article Content
The number of cloud users is rapidly growing in a cloud computing environment, which increases the need for resources. Virtual machine migration, which involves moving the overloaded host to another one, can be used to handle the growing demand for resources successfully. The Bat method, PSO (Particle Swarm Optimization), Cuckoo Search (CS), and Genetic algorithm (GA) are some of the popular meta-heuristics algorithms used in this paper to minimize migration time and makespan value of Virtual Machine(VM) Migration. The main goals of this work are to accomplish VM migration with shorter migration times, smaller makespan values, and higher VM throughput values. Additionally, compare the methods' performance to determine which algorithm is more efficient for minimizing migration time in cloud environments. The fitness value, migration time, makespan, and throughput performance characteristics have been calculated for various task sizes and execution iterations. According to calculated performance characteristics, the Bat algorithm outperformed the other three. The Bat algorithm's migration time is better by 2% to PSO, 6% compared to Cuckoo Search, and 50% compared to GA. Also, when performing VM migration in a cloud computing environment, the Bat algorithm outperforms PSO, CS, and GA in terms of makespan, fitness value, and throughput value.
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Singh, G., and Gupta, P. A review on migration techniques and challenges in live virtual machine migration. 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 542-546, 2016.
Talwani, S., and Singla, J. A Comprehensive Review of Virtual Machine Migration Techniques in Cloud Computing (March 31, 2020). Proceedings of the International Conference on Innovative Computing & Communications (ICICC), 2020.
Kaur, G., and Sachdeva, R. Virtual Machine Migration Approach in Cloud Computing Using Genetic Algorithm. Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, 195-204.
Venkataraman, N. N., and Gopu, A. Virtual Machine Placement Using Multi-Objective Bat Algorithm With Decomposition in Distributed Cloud: MOBA/D for VMP. International Journal of Applied Metaheuristic Computing, 62-77, 2021.
Jeba, G., Paulraj, L., Anand, S., Francis, J., Peter, J. D., Jebadurai, I. J. A combined forecast-based virtual machine migration in cloud data centers. Computers & Electrical Engineering, Elsevier, Volume 69, 287-300, 2018.
Abdelaziz, A., Anastasiadou, M., and Castelli, M. A Parallel Particle Swarm Optimisation for Selecting Optimal Virtual Machine on Cloud Environment. Applied Sciences, 1-25, 2020.
Rodrigues, T. G., Suto, K., Nishiyama, H., and Kato, N. A PSO model with VM migration and transmission power control for low Service Delay in the multiple cloudlets ECC scenario. 2017 IEEE International Conference on Communications (ICC), 1-6, 2017.
Le, T. A survey of live Virtual Machine migration techniques. Computer Science Review, Elsevier, 1-18, 2020.
Shirvani, M. H., Rahmani, A. M., and Sahafi, A. A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: Taxonomy and challenges. Journal of King Saud University - Computer and Information Sciences, vol. 32, 267-286, 2020.
Goyal, K. K., Jain, V., and Verma, P. An Analysis on Virtual Machine Migration Issues and Challenges in Cloud Computing. International Journal of Computer Applications. 25-30, 2018.
Zhang, F., Liu, G., Fu, X., and Yahyapour, R. A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues. IEEE Communications Surveys & Tutorials, vol. 20, no. 2, 1206-1243, 2018.
Akram, S. A., Ghaleb, G. S., Hamaid, S. B., and Vasanthi, V. Survey Study of Virtual Machine Migration Techniques in Cloud Computing. International Journal of Computer Applications, 177. 18-22, 2017.
Liu, F., Ma, Z., Wang, B., and Lin, W. A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center. IEEE Access. 53-67. 2019.
Abubakar, M., Youchang, X., Chengxin, Y., and Ningjiang, C. A Balanced Virtual Machine Migration Mechanism based on Genetic Algorithm. In 2nd International Conference on Machinery, Electronics and Control Simulation. 2017.
Li, S., and Pan, X. Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization. J Wireless Com Network, 1-12, 2020.
Vadivel, R., and Sudalaimuthu, T. Cauchy Particle Swarm Optimization (CPSO) Based Migrations of Tasks in a Virtual Machine. Wireless Pers Commun .1-18, 2021.
Muhammad, K., Gao, S., Qaisar, S., Abdul, M. M., Muhammad, A., Usman, A., Aleena, A., and Shahid, A. Comparative Analysis of Meta-Heuristic Algorithms for Solving Optimization Problems. Proceedings of the 2018 8th International Conference on Management, Education and Information, 612-618, 2018.
Karthikeyan, K., Sunder, R., Shankar, K., Lakshmanaprabu, S. K., Vijayakumar, V., Elhoseny, M., and Manogaran, G. Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA). J Supercomput , 3374–3390, 2020.
Naik, B. B., Singh, D., and Samaddar, A. B. FHCS: Hybridized optimization for virtual machine migration and task scheduling in cloud data center. IET Commun., 1942-1948, 2020.
Jiang, Y., Wang, J., Shi, J., Zhu, J., and Teng, L. Network-aware Virtual Machine Migration Based on Gene Aggregation Genetic Algorithm. Mobile Netw Appl 25, 1457–1468, 2020.
Nashaat, H., Ashry, N., and Rizk, R. Smart elastic scheduling algorithm for virtual machine migration in cloud computing. The Journal of Supercomputing 75, 3842–3865, 2019.
Singh, S., and Singh, D. A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization. ICT Express, elsevier, 1-8, 2022.
Kaur, K., and Kumar, Y. Swarm Intelligence and its applications towards Various Computing: A Systematic Review. 2020 International Conference on Intelligent Engineering and Management (ICIEM), 57-62, 2020.
Braiki, K., and Youssef, H. Data Center Resource Provisioning Using Particle Swarm Optimization and Cuckoo Search: A Performance Comparison. In book: Advanced Information Networking and Applications.1138-1149. 2020.
Joshi, A. S., Kulkarni, O., Kakandikar, G. M., Nandedkar, V. M. Cuckoo Search Optimization- A Review. Materials Today: Proceedings, vol. 4, 7262-7269, 2017.
Katoch, S., Chauhan, S. S., and Kumar, V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80, 8091–8126 2021.