Towards Energy-efficient Cloud Computing: A Review of Network-Aware VM Placement Approaches
Subject Areas : Cloud computing
Ali Baydoun
1
*
,
Ahmed S Zekri
2
1 - Department of Mathematics & Computer Science, Beirut Arab University, Lebanon
2 - Department of Mathematics & Computer Science, Alexandria University, Egypt
Keywords: Cloud computing, VM placement, network-aware, Energy-efficient, Network architecture,
Abstract :
Cloud data centers (CDCs) have witnessed significant growth to meet the increasing demands of modern applications. However, this expansion has raised concerns regarding the environmental impact, energy requirements, and electricity costs associated with data centers. The network infrastructure, serving as the communication backbone of these data centers, plays a crucial role in their scalability, performance, cost, and, most importantly, energy consumption. This review provides meaningful perspectives and valuable insights into the state-of-the-art research regarding the problem of virtual machine placement (VMP), focusing on the network-aware energy efficiency aspects of data centers. It provides an overview of VM placement and presents a comprehensive survey of prominent VM placement algorithms from the existing literature. Additionally, a thematic taxonomy of network-aware algorithms is introduced, highlighting the key energy consumption metrics and presenting a new classification of VMP algorithms that considers datacenter network (DCN) topology, traffic patterns, communication patterns, and energy reduction strategies. Besides addressing pertinent research questions in this domain, this review summarizes the findings and suggests potential avenues for future research, guiding researchers in designing and implementing more effective and efficient network-aware VM placement algorithms that optimize energy consumption, improve network performance, and minimize migration costs.
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[2] D. Bliedy, S. Mazen, and E. Ezzat, “Datacentre Total Cost of Ownership (TCO) Models : A Survey,” International Journal of Computer Science, Engineering and Applications, vol. 8, no. 2/3/4, pp. 47–62, 2018, doi: 10.5121/ijcsea.2018.8404.
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[4] L. Zhou, C. H. Chou, L. N. Bhuyan, K. K. Ramakrishnan, and D. Wong, “Joint server and network energy saving in data centers for latency-sensitive applications,” Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, pp. 700–709, 2018, doi: 10.1109/IPDPS.2018.00079.
[5] K. Bilal et al., “A survey on Green communications using Adaptive Link Rate,” Cluster Comput, vol. 16, no. 3, pp. 575–589, Jul. 2013, doi: 10.1007/s10586-012-0225-8.
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[7] M. H. Ferdaus, M. Murshed, R. N. Calheiros, and R. Buyya, Network-aware virtual machine placement and migration in cloud data centers, no. May. 2015. doi: 10.4018/978-1-4666-8213-9.ch002.
[8] A. Hammadi and L. Mhamdi, “A survey on architectures and energy efficiency in Data Center Networks,” Comput Commun, vol. 40, pp. 1–21, 2014, doi: 10.1016/j.comcom.2013.11.005.
[9] K. Bilal et al., “A taxonomy and survey on Green Data Center Networks,” Future Generation Computer Systems, vol. 36, pp. 189–208, Jul. 2014, doi: 10.1016/j.future.2013.07.006.
[10] R. W. Ahmad, A. Gani, S. H. A. Hamid, M. Shiraz, A. Yousafzai, and F. Xia, “A survey on virtual machine migration and server consolidation frameworks for cloud data centers,” Journal of Network and Computer Applications, vol. 52, pp. 11–25, 2015, doi: 10.1016/j.jnca.2015.02.002.
[11] F. L. Pires and B. Baran, “A virtual machine placement taxonomy,” Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, no. July, pp. 159–168, 2015, doi: 10.1109/CCGrid.2015.15.
[12] M. Masdari, S. S. Nabavi, and V. Ahmadi, “An overview of virtual machine placement schemes in cloud computing,” Journal of Network and Computer Applications, vol. 66, pp. 106–127, 2016, doi: 10.1016/j.jnca.2016.01.011.
[13] H. Talebian et al., Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues, vol. 23, no. 2. Springer US, 2020. doi: 10.1007/s10586-019-02954-w.
[14] H. Zhuang and B. Esmaeilpour Ghouchani, “Virtual machine placement mechanisms in the cloud environments: a systematic review,” Kybernetes, vol. 50, no. 2, pp. 333–368, 2021, doi: 10.1108/K-09-2019-0635.
[15] L. Helali and M. N. Omri, “A survey of data center consolidation in cloud computing systems,” 2021. doi: 10.1016/j.cosrev.2021.100366.
[16] A. Sumathi, … B. K.-T. J. of, and undefined 2023, “Advancements in Energy-Efficient Virtual Machine Placement Survey for Cloud Computing,” Researchgate.Net, no. February, 2024, doi: 10.13140/RG.2.2.17918.36164.
[17] N. Rana et al., “A systematic literature review on contemporary and future trends in virtual machine scheduling techniques in cloud and multi-access computing,” Front Comput Sci, vol. 6, 2024, doi: 10.3389/fcomp.2024.1288552.
[18] J. Zou, K. Wang, K. Zhang, and M. Kassim, “Perspective of virtual machine consolidation in cloud computing: a systematic survey,” Telecommun Syst, p. 11235, 2024, doi: 10.1007/s11235-024-01184-9.
[19] S. R. Swain, A. Parashar, A. K. Singh, and C. Nan Lee, “An Energy Efficient Virtual Machine Placement Scheme for Intelligent Resource Management at Cloud Data Center,” in OCIT 2023 - 21st International Conference on Information Technology, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 65–70. doi: 10.1109/OCIT59427.2023.10430915.
[20] S. Kumar, S. Mittal, and M. Singh, “Active VM Placement Approach Based on Energy Efficiency in Cloud Environment,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2022, pp. 35–46. doi: 10.1007/978-981-19-1018-0_4.
[21] Z. Li, K. Lin, S. Cheng, L. Yu, and J. Qian, “Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers,” J Grid Comput, vol. 20, no. 4, 2022, doi: 10.1007/s10723-022-09631-0.
[22] H. Xing, J. Zhu, R. Qu, P. Dai, S. Luo, and M. A. Iqbal, “An ACO for energy-efficient and traffic-aware virtual machine placement in cloud computing,” Swarm Evol Comput, vol. 68, no. November 2021, p. 101012, 2022, doi: 10.1016/j.swevo.2021.101012.
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