(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-8 Issue-75 February-2021
Full-Text PDF
Paper Title : An effective energy-efficient virtual machine placement using clonal selection algorithm
Author Name : Saiful Izwan Suliman, Hazrien Nazman, Afdallyna Fathiyah Harun, Roslina Mohamad, Murizah Kassim, Farah Yasmin, Abdul Rahman and Yuslinda Wati Mohamad Yusof
Abstract :

Virtual machine (VM) placement is a process of server consolidation at the same time optimizing tasks execution in an energy-efficient environment. This process occurs in data centre which manages a number of physical machines independently. In the past decade, many research investigating virtual machine placement problem have been conducted extensively. However, most of the research focus on the energy consumptions by physical machines in a data centre. In reality, communication activities inter and intra networks is also consuming energy that should be taken into the consideration when planning for VM placement. Therefore, these two types of energy should be considered and managed in parallel during the virtual machine placement execution in order to produce energy-efficient environment. In this paper, we propose the use of Clonal Selection Algorithm (CSA) in handling virtual machine placement task which takes into consideration energy consumptions in both servers and communication network in data centre. The obtained results from the simulations produced the lowest consumption of 2219 energy unit, thus highlighting the efficiency of the proposed algorithm on the tested problem instances of different types with different complexity.

Keywords : Virtual machine placement, Clonal selection algorithm, Optimization.
Cite this article : Suliman SI, Nazman H, Harun AF, Mohamad R, Kassim M, Yasmin F, Rahman A, Yusof YW. An effective energy-efficient virtual machine placement using clonal selection algorithm. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(75):412-421. DOI:10.19101/IJATEE.2020.762129.
References :
[1]Duong-Ba TH, Nguyen T, Bose B, Tran TT. A dynamic virtual machine placement and migration scheme for data centers. IEEE Transactions on Services Computing. 2018:1-11.
[Crossref] [Google Scholar]
[2]Pires FL, Barán B. A Virtual machine placement taxonomy. In 15th IEEE/ACM international symposium on cluster, cloud and grid computing 2015 (pp. 159-68). IEEE.
[Crossref] [Google Scholar]
[3]Jonas E, Schleier-Smith J, Sreekanti V, Tsai CC, Khandelwal A, Pu Q, et al. Cloud programming simplified: a berkeley view on serverless computing. arXiv preprint arXiv:1902.03383. 2019.
[Google Scholar]
[4]Choudhary A, Rana S, Matahai KJ. A critical analysis of energy efficient virtual machine placement techniques and its optimization in a cloud computing environment. Procedia Computer Science. 2016; 78:132-8.
[Crossref] [Google Scholar]
[5]Shaw R, Howley E, Barrett E. An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simulation Modelling Practice and Theory. 2019; 93:322-42.
[Crossref] [Google Scholar]
[6]Zhao H, Wang J, Liu F, Wang Q, Zhang W, Zheng Q. Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Transactions on Parallel and Distributed Systems. 2018; 29(6):1385-400.
[Crossref] [Google Scholar]
[7]Silva Filho MC, Monteiro CC, Inácio PR, Freire MM. Approaches for optimizing virtual machine placement and migration in cloud environments: a survey. Journal of Parallel and Distributed Computing. 2018; 111:222-50.
[Crossref] [Google Scholar]
[8]Liu XF, Zhan ZH, Deng JD, Li Y, Gu T, Zhang J. An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation. 2016; 22(1):113-28.
[Crossref] [Google Scholar]
[9]Zhao DM, Zhou JT, Li K. An energy-aware algorithm for virtual machine placement in cloud computing. IEEE Access. 2019; 7:55659-68.
[Crossref] [Google Scholar]
[10]Ahmad A, Zainudin WS, Kama MN, Idris NB, Saudi MM. Cloud Co-residency denial of service threat detection inspired by artificial immune system. In proceedings of the artificial intelligence and cloud computing conference 2018 (pp. 76-82).
[Crossref] [Google Scholar]
[11]Hofmeyr SA, Forrest S. Architecture for an artificial immune system. Evolutionary Computation. 2000; 8(4):443-73.
[Crossref] [Google Scholar]
[12]Rashid N, Iqbal J, Mahmood F, Abid A, Khan US, Tiwana MI. Artificial immune system–negative selection classification algorithm (NSCA) for four class electroencephalogram (EEG) signals. Frontiers in Human Neuroscience. 2018; 12:1-15.
[Crossref] [Google Scholar]
[13]Luo W, Lin X, Zhu T, Xu P. A clonal selection algorithm for dynamic multimodal function optimization. Swarm and Evolutionary Computation. 2019; 50:100459.
[Crossref] [Google Scholar]
[14]Zamani MK, Musirin I, Omar MS, Suliman SI, Ghani NA, Kamari NA. Gravitational search algorithm based technique for voltage stability improvement. Indonesian Journal of Electrical Engineering and Computer Science. 2018; 9(1):123-30.
[Google Scholar]
[15]Donyagard Vahed N, Ghobaei‐Arani M, Souri A. Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: a comprehensive review. International Journal of Communication Systems. 2019; 32(14):1-32.
[Crossref] [Google Scholar]
[16]Suliman SI, Kendall G, Musirin I. Artificial immune algorithm in solving the channel assignment task. In international conference on control system, computing and engineering (ICCSCE 2014) 2014 (pp. 153-8). IEEE.
[Crossref] [Google Scholar]
[17]Parvizi E, Rezvani MH. Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach. Cluster Computing. 2020:1-23.
[Crossref] [Google Scholar]
[18]Suliman SI, Rahman TA. Artificial immune system based machine learning for voltage stability prediction in power system. In international power engineering and optimization conference 2010 (pp. 53-8). IEEE.
[Crossref] [Google Scholar]
[19]Chen D, Li S, Wang J, Feng Y, Liu Y. A multi-objective trajectory planning method based on the improved immune clonal selection algorithm. Robotics and Computer-Integrated Manufacturing. 2019; 59:431-42.
[Crossref] [Google Scholar]