An Evaluation of New Meta-heuristics for Virtual Machine Placement in Cloud Data Centers | ||
| Journal of AI and Data Mining | ||
| دوره 13، شماره 1، فروردین 2025، صفحه 1-10 اصل مقاله (1.74 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.14987.2597 | ||
| نویسندگان | ||
| Mohsen Kiani1؛ Mohammad Reza Khayyambashi* 2 | ||
| 1Department of Computer Science, Khansar Campus, University of Isfahan, Isfahan, Iran. | ||
| 2Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran. | ||
| چکیده | ||
| The present study investigates the effectiveness of several new meta-heuristic (MH) methods in solving virtual machine (VM) to physical machine (PM) placement (VMP) in cloud data centers. More specifically, Coati optimization algorithm (COA) is properly adapted for solving VMP by introducing several operators for the phases of the algorithm. Several emerging and classic meta-heuristics are also included in the evaluations, including genetic algorithm, chemical reaction optimization, Harris hawk optimization (HHO), and electron valley optimizer (EVO). Two main parameters are included in our evaluations, including power consumption and resource wastage. The algorithms are evaluated in terms of their ability to reduce power consumption and resource wastage in VMP, and also in terms of their execution times. A set of evaluations with synthetic VMs are performed. The results indicate that all MHs perform almost similarly, while emerging methods (COA, HHO, EVO) have a marginal benefit. | ||
| کلیدواژهها | ||
| Cloud Computing؛ Coati Optimization Algorithm؛ Cloud Date Center؛ Virtual Machine | ||
| مراجع | ||
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[1] M. Singh, “Virtualization in cloud computing-a study,” in 2018 International Conference on Advances in Computing, Communication Control and Networking, ICACCCN, 2018, pp. 64-67.
[2] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
[3] Y. Xing and Y. Zhan, “Virtualization and cloud computing,” in Future Wireless Networks and Information Systems, Lecture Notes in Electrical Engineering, 2012, pp. 305-312.
[4] J. D. Ullman, “NP-complete scheduling problems," Journal of Computer and System Sciences, vol. 10, no. 3, pp. 384-393, 1975.
[5] J. P. Gabhane, S. Pathak, and N. M. Thakare, “Metaheuristics algorithms for virtual machine placement in cloud computing environments—a review,” in Computer Networks, Big Data and IoT, ICCBI, 2020, pp. 329-349.
[6] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67-82, 1997.
[7] B. Pourghebleh, A. Aghaei Anvigh, A. R. Ramtin, and B. Mohammadi, “The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments,” Cluster Computing, vol. 24, no. 3, pp. 2673-2696, 2021.
[8] K. Rajwar, K. Deep, and S. Das, “An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges,” Artificial Intelligence Review, vol. 56, pp. 1-71, 2023.
[9] H. Talebian, A. Gani, M. Sookhak, A. A. Abdelatif, A. Yousafzai, A. V. Vasilakos, and F. R. Yu, “Optimizing virtual machine placement in iaas data centers: taxonomy, review and open issues,” Cluster Computing, vol. 23, pp. 837-878, 2020.
[10] A. Safari-Bavil, S. Jabbehdari, and M. Ghobaei-Arani, “An Efficient Approach to Solve Software-defined Networks based Virtual Machines Placement Problem using Moth-Flame Optimization in the Cloud Computing Environment,” Journal of AI and Data Mining, vol. 9, no. 3, pp. 309-320, 2021.
[11] F. Alharbi, Y.-C. Tian, M. Tang, W.-Z. Zhang, C. Peng, and M. Fei, “An ant colony system for energy-efficient dynamic virtual machine placement in data centers,” Expert Systems with Applications, vol. 120, pp. 228-238, 2019.
[12] M. Kiani and M. R. Khayyambashi, “A network-aware and power-efficient virtual machine placement scheme in cloud datacenters based on chemical reaction optimization,” Computer Networks, vol. 196, pp. 108270, 2021.
[13] Z. Li, Y. Li, T. Yuan, S. Chen, and S. Jiang, “Chemical reaction optimization for virtual machine placement in cloud computing,” Applied Intelligence, vol. 49, pp. 220-232, 2019.
[14] M. HS, P. Gupta, and G. McArdle, “A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers,” Plos One, vol. 18, no. 8, p. e0289156, 2023.
[15] B. Zhang, X. Wang, and H. Wang, “Virtual machine placement strategy using cluster-based genetic algorithm,” Neurocomputing, vol. 428, pp. 310-316, 2021.
[16] L. T. Duan, J. Wang, and H. Y. Wang, “An energy-aware ant colony optimization strategy for virtual machine placement in cloud computing,” Cluster Computing, vol. 27, no. 10, pp. 1-14, 2024.
[17] M. Kiani, “Virtual machine placement in cloud data centers using energy valley optimizer algorithm,” Tabriz Journal of Electrical Engineering, in press, 2024.
[18] A. Y. Lam and V. O. Li, “Chemical reaction optimization: a tutorial,” Memetic Computing, vol. 4, pp. 3-17, 2012.
[19] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems, vol. 97, pp. 849-872, 2019.
[20] M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, “Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 259, pp. 110011, 2023.
[21] M. Azizi, U. Aickelin, H. A. Khorshidi, and M. Baghalzadeh Shishehgarkhaneh, “Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization,” Scientific Reports, vol. 13, no. 1, pp. 226, 2023.
[22] X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” ACM SIGARCH Computer Architecture News, vol. 35, no. 2, pp. 13-23, 2007.
[23] Y. Ajiro and A. Tanaka, “Improving packing algorithms for server consolidation,” in International Conference on Mathematical Geophysics, CMG, 2007, vol. 253, pp. 399-406. | ||
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