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Volume 14, Issue 2 (6-2022)
itrc 2022, 14(2): 14-22 |
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Monshizadeh Naeen H. Cost Reduction Using SLA-Aware Genetic Algorithm for Consolidation of Virtual Machines in Cloud Data Centers. itrc 2022; 14 (2) :14-22
URL: http://journal.itrc.ac.ir/article-1-519-en.html
URL: http://journal.itrc.ac.ir/article-1-519-en.html
Department of Computer and Information Technology Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran , monshizade@gmail.com
Abstract: (1982 Views)
Cloud computing is a computing model which uses network facilities to provision, use and deliver computing services. Nowadays, the issue of reducing energy consumption has become very important alongside the efficiency for Cloud service providers. Dynamic virtual machine (VM) consolidation is a technology that has been used for energy efficient computing in Cloud data centers. In this paper, we offer solutions to reduce overall costs, including energy consumption and service level agreement (SLA) violation. To consolidate VMs into a smaller number of physical machines, a novel SLA-aware VM placement method based on genetic algorithms is presented. In order to make the VM placement algorithm be SLA-aware, the proposed approach considers workloads as non-stationary stochastic processes, and automatically approximates them as stationary processes using a novel dynamic sliding window algorithm. Simulation results in the CloudSim toolkit confirms that the proposed virtual server consolidation algorithms in this paper provides significant total cost savings (evaluated by ESV metric), which is about 45% better than the best of the benchmark algorithms.
Keywords: component, Cloud Computing, Green IT, SLA violation, VM Consolidation, Genetic Algorithms.
References
1. Jennings, B. and R. Stadler, Resource management in clouds: Survey and research challenges. Journal of Network and Systems Management, 2015. 23(3): p. 567-619.
2. Beloglazov, A., J. Abawajy, and R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 2012. 28(5): p. 755-768.
3. Andrae, A.S. and T. Edler, On global electricity usage of communication technology: trends to 2030. Challenges, 2015. 6(1): p. 117-157.
4. Monshizadeh Naeen, H., E. Zeinali, and A. Toroghi Haghighat, Adaptive Markov‐based approach for dynamic virtual machine consolidation in cloud data centers with quality‐of‐service constraints. Software: Practice and Experience, 2020. 50(2): p. 161-183.
5. Van Steen, M. and A.S. Tanenbaum, Distributed systems. 2017: Maarten van Steen Leiden, The Netherlands.
6. Mishra, M., et al., Dynamic resource management using virtual machine migrations. IEEE Communications Magazine, 2012. 50(9): p. 34-40.
7. Clark, C., et al. Live migration of virtual machines. in Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation-Volume 2. 2005. USENIX Association.
8. Beloglazov, A. and R. Buyya, Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 2012. 24(13): p. 1397-1420.
9. Voorsluys, W., et al., Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation. CloudCom, 2009. 9: p. 254-265.
10. Zolfaghari, R., et al., An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification. Software: Practice and Experience, 2022. 52(1): p. 194-235.
11. Arianyan, E., H. Taheri, and V. Khoshdel, Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. Journal of Network and Computer Applications, 2017. 78: p. 43-61.
12. Li, L., et al., SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. IEEE Access, 2019. 7: p. 9490-9500.
13. Mustafa, S., et al., Sla-aware best fit decreasing techniques for workload consolidation in clouds. IEEE Access, 2019. 7: p. 135256-135267.
14. Barthwal, V. and M.M.S. Rauthan, AntPu: a meta-heuristic approach for energy-efficient and SLA aware management of virtual machines in cloud computing. Memetic Computing, 2021. 13(1): p. 91-110.
15. Monshizadeh Naeen, H., E. Zeinali, and A. Toroghi Haghighat, A stochastic process-based server consolidation approach for dynamic workloads in cloud data centers. The Journal of Supercomputing, 2020. 76(3): p. 1903-1930.
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