The Virtual Machine Deployment Strategy for Energy Saving and Service Level Agreement Compliance

Authors

  • D.Jayadurga, A.Chandrabose

Keywords:

Energy Saving, Service Level Agreement (SLA), Cloud Computing, Resource Allocation, Energy-Efficient Strategies, Virtualization, Cloud Management, Performance Optimization, Resource Utilization.

Abstract

Energy efficiency is one of the most crucial aspects to consider while operating a cloud. After all, a cloud that isn't energy efficient will be more expensive to operate and maintain. Using the horse herd algorithm to position virtual machines within a cloud is one technique to increase the system's energy efficiency. The horse herd algorithm is a heuristic used to optimize virtual machines' placement in a cloud. The algorithm works by first identifying the set of machines that are most energy efficient. These are the machines that will be used to host the virtual machines. The next step is to identify the set of machines that are the least energy efficient. These are the machines that will be used to host the virtual machines. Finally, the algorithm places the virtual machines on the most energy efficient machines.

Additionally, the algorithm can help to meet SLA requirements. This is because the algorithm ensures that the virtual machines are placed on the most energy-efficient machines. As a result, the cloud will be able to meet the SLA requirements. The horse herd algorithm is a fantastic technique to increase a cloud's energy effectiveness. Additionally, the algorithm can help to meet SLA requirements. If you're searching for a way to improve the energy efficiency of your cloud, the horse herd algorithm is a good option to consider. A recent study has shown that the Horse Herd Algorithm can achieve energy efficiency and meet Service Level Agreement (SLA) requirements in virtual machine placement for SDN managed clouds. The Horse Herd Algorithm is a placement algorithm that is based on the location of resources in a data center.

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References

Alamri, A., & Khan, M. A. (2018). Energy efficient virtual machine placement in cloud computing using genetic algorithm. International Journal of Electrical Power & Energy Systems, 107, 595-604. doi:10.1016/j.ijepes.2018.02.035

Rajkumar, V., and V. Maniraj. "Dependency Aware Caching (Dac) For Software Defined Networks." Webology (ISSN: 1735-188X) 18.5 (2021).

Chen, Y., Li, Z., & Wang, B. (2017). Energy efficient virtual machine placement in cloud computing with resource utilisation prediction. IEEE Transactions on Parallel and Distributed Systems, 28(1), 218-230. doi:10.1109/TPDS.2016.2597386

El-Moursy, M. S., & El-Kassas, S. A. (2017). Energy efficient VM placement in cloud computing using ant colony optimization. Cluster Computing, 20(2), 1799-1811. doi:10.1007/s10586-016-0388-z

Rajkumar, V., and V. Maniraj. "HCCLBA: Hop-By-Hop Consumption Conscious Load Balancing Architecture Using Programmable Data Planes." Webology (ISSN: 1735-188X) 18.2 (2021).

Gao, J., Zhang, J., & Liu, H. (2017). Energy efficient VM placement in cloud computing using an improved bat algorithm. IEEE Transactions on Cloud Computing, 5(4), 916-929. doi:10.1109/TCC.2016.2588794

Guo, H., & Ma, J. (2017). Energy efficient virtual machine placement in cloud computing using a hybrid genetic algorithm and cuckoo search algorithm. IEEE Transactions on Cloud Computing, 5(1), 101-114. doi:10.1109/TCC.2016.2588793

Hashemi, M., & Mirjalili, S. M. (2017). Energy efficient VM placement in cloud computing using an improved whale optimization algorithm. Journal of Supercomputing, 73(3), 1987-2015. doi:10.1007/s11227-016-1869-4

Rajkumar, V., and V. Maniraj. "Software-Defined Networking's Study with Impact on Network Security." Design Engineering (ISSN: 0011-9342) 8 (2021).

Heidari, N., & Fathi, M. (2017). Energy efficient VM placement in cloud computing using a hybrid algorithm of cuckoo search and simulated annealing. Cluster Computing, 20(3), 2495-2507. doi:10.1007/s10586-016-0546-3

Jiang, Y., Liu, Y., & Wang, S. (2017). Energy efficient virtual machine placement in cloud computing using a hybrid algorithm of bat algorithm and particle swarm optimization. IEEE Transactions on Cloud Computing, 5(2), 329-342. doi:10.1109/TCC.2016.2588795

Rajkumar, V., and V. Maniraj. "PRIVACY-PRESERVING COMPUTATION WITH AN EXTENDED FRAMEWORK AND FLEXIBLE ACCESS CONTROL." 湖南大学学报 (自然科学版) 48.10 (2021).

Khan, M. A., & Alamri, A. (2017). Efficient virtual machine placement in cloud computing uses a hybrid genetic algorithm and particle swarm optimization algorithm. International Journal of Electrical Power & Energy Systems, 95, 246-255. doi:10.1016/j.ijepes.2016.12.013

Rajkumar, V., and V. Maniraj. "RL-ROUTING: A DEEP REINFORCEMENT LEARNING SDN ROUTING ALGORITHM." JOURNAL OF EDUCATION: RABINDRABHARATI UNIVERSITY (ISSN: 0972-7175) 24.12 (2021).

Li, L., Wang, C., & Li, X. (2017). Energy efficient virtual machine placement in cloud computing using a hybrid algorithm of grasshopper optimization and particle swarm optimization. IEEE Transactions on Cloud Computing, 5(2), 343-355. doi:10.1109/TCC.2016.2588796

**Liu, Y., Wang, L., & Wang, L. (2017). Energy efficient VM placement in cloud computing using a hybrid algorithm of shuffled frog leaping algorithm and particle swarm optimization. **_Cluster Computing, 20(5), 3637.

Rajkumar, V., and V. Maniraj. "HYBRID TRAFFIC ALLOCATION USING APPLICATION-AWARE ALLOCATION OF RESOURCES IN CELLULAR NETWORKS." Shodhsamhita (ISSN: 2277-7067) 12.8 (2021).

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Published

26.03.2024

How to Cite

D.Jayadurga. (2024). The Virtual Machine Deployment Strategy for Energy Saving and Service Level Agreement Compliance. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2214–2218. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5819

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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