Enhancement of ACOTS Algorithm for Virtual Machine Placements in Cloud Data Centers

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 12
Year of Publication : 2024
Authors : Jyotsna P. Gabhane, Sunil Pathak, Nita Thakare
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How to Cite?

Jyotsna P. Gabhane, Sunil Pathak, Nita Thakare, "Enhancement of ACOTS Algorithm for Virtual Machine Placements in Cloud Data Centers," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 12, pp. 32-43, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I12P104

Abstract:

Since many cloud data centers provide worldwide services on demand, it has been a popular research topic in recent years. Users and virtual machines grow in the number directly to data centers’ expanding size. Virtual Machine Placement methods are mainly used in data centers to consolidate servers. Therefore, the placement of virtual machines is the most active research area today. The performance of VM Placements in cloud computing depends on various factors, including resource management, power consumption, and others. This paper provides the optimal solution for VM placements in cloud environments using ACOTS (Ant Colony Optimization with Tabu Search) hybrid metaheuristic algorithm enhancing with the feature of modified Eagle strategy. The hybridization leverages ACO’s global search capabilities, TS’s memory-driven diversification, and the adaptive exploration-exploitation balance of EagleMOD (modified EAGLE strategy). By integrating these techniques, the proposed algorithm enhances convergence speed and resolves the issue of local optima, ensuring robust performance across various scenarios.

Keywords:

Cloud computing, Meta-heuristic algorithms, Optimal solution, Resource management, Virtual machine placement, Power consumption.

References:

[1] Richa, and Deepika Kurkreja, “Optimizing Task Scheduling for Energy Aware Networks,” 14th International Conference on Cloud Computing, Data Science & Engineering, Noida, India, pp. 297-302, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Krishna Gopal Dhal et al., “Eagle Strategy in Nature-Inspired Optimization: Theory, Analysis, Applications, and Comparative Study,” Archives of Computational Methods in Engineering, vol. 31, pp. 1213-1232, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Peter Mell, and Timothy Grance, “NIST SP 800-145, The NIST Definition of Cloud Computing,” National Institute of Standards and Technology Gaithersburg, pp. 1-7, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Patricia T. Endo et al., “High Availability in Clouds: Systematic Review and Research Challenges,” Journal of Cloud Computing, vol. 5, pp. 1-15, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Qi Zhang, Lu Cheng, and Raouf Boutaba, “Cloud Computing: State-of-the-Art and Research Challenges,” Journal of Internet Services and Applications, vol. 1, pp. 7-18, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ammar Al-Moalmi et al., “Optimal Virtual Machine Placement Based on Grey Wolf Optimization,” Electronics, vol. 8, no. 3, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Zoltán Ádám Mann, “Allocation of Virtual Machines in Cloud Data Centers-A Survey of Problem Models and Optimization Algorithms,” ACM Computing Surveys, vol. 48, no. 1, pp. 1-34, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Montassar Riahi, and Saoussen Krichen, “A Multi-Objective Decision Support Framework for Virtual Machine Placement in Cloud Data Centers: A Real Case Study,” The Journal of Supercomputing, vol. 74, pp. 2984-3015, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Fabio Lopez-Pires, and Benjamin Baran, “Virtual Machine Placement Literature Review,” Arxiv, pp. 1-11, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Nasim Donyagard Vahed, Mostafa Ghobaei-Arani, and Alireza Souri, “Multiobjective Virtual Machine Placement Mechanisms Using Nature-Inspired Metaheuristic Algorithms in Cloud Environments: A Comprehensive Review,” International Journal of Communication Systems, vol. 32, no. 14, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Bingdong Li et al., “Many-Objective Evolutionary Algorithms: A Survey,” ACM Computing Surveys, vol. 48, no. 1, pp. 1-35, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Fabio López-Pires, and Benjamín Barán, “Many-Objective Virtual Machine Placement,” Journal of Grid Computing, vol. 15, pp. 161-176, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Alessandro Mattiussi, Michele Rosano, and Patrizia Simeoni, “A Decision Support System for Sustainable Energy Supply Combining Multi-Objective and Multi-Attribute Analysis: An Australian Case Study,” Decision Support Systems, vol. 57, pp. 150-159, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zoha Usmani, and Shailendra Singh, “A Survey of Virtual Machine Placement Techniques in a Cloud Data Center,” Procedia Computer Science, vol. 78, pp. 491-498, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Kumar Nishant et al., “Load Balancing of Nodes in Cloud Using Ant Colony Optimization,” UKSim 14th International Conference on Computer Modelling and Simulation, Cambridge, UK, pp. 3-8, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] edhat A. Tawfeek et al., “Virtual Machine Placement Based on Ant Colony Optimization for Minimizing Resource Wastage,” Advanced Machine Learning Technologies and Applications, Communications in Computer and Information Science, vol. 488, pp. 153-164, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Xiao-Fang Liu et al., “An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing,” IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 113-128, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Keng-Mao Cho et al., “A Hybrid Meta-Heuristic Algorithm for VM Scheduling with Load Balancing in Cloud Computing,” Neural Computing and Applications, vol. 26, pp. 1297-1309, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Qinghua Zheng et al., “Multi-objective Optimization Algorithm Based on BBO for Virtual Machine Consolidation Problem,” IEEE 21st International Conference on Parallel and Distributed Systems, Melbourne, VIC, Australia, pp. 414-421, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Da-Ming Zhao, Jian-Tao Zhou, and Keqin Li, “An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing,” IEEE Access, vol. 7, pp. 55659-55668, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Esha Barlaskar, Yumnam Jayanta Singh, and Biju Issac, “Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centres,” International Journal of Grid and Utility Computing, vol. 9, no. 1, pp. 1-17, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Weichao Ding et al., “DFA-VMP: An Efficient and Secure Virtual Machine Placement Strategy under Cloud Environment,” Peer-to-Peer Networking and Applications, vol. 11, pp. 318-333, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sara Mejahed, and M. Elshrkawey, “A Multi-Objective Algorithm for Virtual Machine Placement in Cloud Environments Using a Hybrid of Particle Swarm Optimization and Flower Pollination Optimization,” PeerJ Computer Science, vol. 8, pp. 1-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Masoud Hashemi et al., “A Multi-Objective Method for Virtual Machines Allocation in Cloud Data Centres Using an Improved Grey Wolf Optimization Algorithm,” IET Communications, vol. 15, no. 18, pp. 2342-2353, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Chaoqiang Jin et al., “A Review of Power Consumption Models of Servers in Data Centers,” Applied Energy, vol. 265, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] A.S. Abohamama, and Eslam Hamouda, “A Hybrid Energy-Aware Virtual Machine Placement Algorithm for Cloud Environments,” Expert Systems with Applications, vol. 150, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Yongquan Zhou, Ying Ling, and Qifang Luo, “Lévy Flight Trajectory-Based Whale Optimization Algorithm for Engineering Optimization,” Engineering Computations, vol. 35, no. 7, pp. 2406-2428, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Xin-She Yang, and Suash Deb, “Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization,” Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, vol. 284, pp. 101-111, 2010.[CrossRef] [Google Scholar] [Publisher Link]