An Energy-Aware Approach to Virtual Machine Consolidation Using Classification and the Dragonfly Algorithm in Cloud Data Centers
الموضوعات :
Nastaran Evaznia
1
,
Reza Ebrahimi
2
,
davoud bahrepour
3
1 - Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2 - Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
3 - Department of Computer, mashhad Branch, Islamic Azad University, mashhad, Iran
الکلمات المفتاحية: Cloud computing, Consolidation, Quartile parameter, Dragonfly algorithm, SLA violations, Migrations, Energy consumption,
ملخص المقالة :
Nowadays, reducing energy consumption in cloud computing is of great interest due to the high operational costs and its impact on climate change. The consolidation solution is an effective method for minimizing the number of physical machines (PMs) and reducing energy consumption. The virtual machine (VM) consolidation process encounters the challenge of reducing energy consumption while effectively managing resource allocation. The aim of this paper is to address these challenges through the classification of PMs and the use of the dragonfly algorithm. The quartile parameter is utilized to classify PMs into three categories: underloaded, medium load, and overloaded. First, we identified the overloaded PMs in the overloaded category. Then, we presented a solution to select virtual machines from an overloaded PM based on resource usage. Additionally, the Dragonfly algorithm is utilized to select destinations for hosting migrant virtual machines in the medium load category. Furthermore, we identified underloaded PMs in the underloaded categories using this algorithm. The proposed solution is evaluated using the CloudSim toolkit and tested with workloads consisting of over a thousand data points from virtual machines based on PlanetLab data. The results from the simulation experiments indicate that the proposed solution, while avoiding SLA violations and minimizing additional migrations, has significantly reduced energy consumption.
[1] T. Alam, “Cloud Computing and Its Role in the Information Technology,” SSRN Electron. J., vol. 1, no. 2, pp. 108–115, 2020.
[2] M. Yenugula, S. Sahoo, and S. Goswami, “Cloud computing for sustainable development: An analysis of environmental, economic and social benefits,” J. Futur. Sustain., vol. 4, no. 1, pp. 59–66, 2024.
[3] M. M. Sadeeq, N. M. Abdulkareem, S. R. M. Zeebaree, D. M. Ahmed, A. S. Sami, and R. R. Zebari, “IoT and Cloud computing issues, challenges and opportunities: A review,” Qubahan Acad. J., vol. 1, no. 2, pp. 1–7, 2021.
[4] E. Jonas et al., “Cloud programming simplified: A berkeley view on serverless computing,” arXiv Prepr. arXiv1902.03383, 2019.
[5] W. Yao, Z. Wang, Y. Hou, X. Zhu, X. Li, and Y. Xia, “An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment,” Futur. Gener. Comput. Syst., vol. 146, pp. 222–233, 2023.
[6] R. Shaw, E. Howley, and E. Barrett, “An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions,” Simul. Model. Pract. Theory, vol. 93, pp. 322–342, 2019.
[7] C. Thiam and F. Thiam, “Energy efficient cloud data center using dynamic virtual machine consolidation algorithm,” in Business Information Systems: 22nd International Conference, BIS 2019, Seville, Spain, June 26–28, 2019, Proceedings, Part I 22, Springer, 2019, pp. 514–525.
[8] L. Helali and M. N. Omri, “A survey of data center consolidation in cloud computing systems,” Comput. Sci. Rev., vol. 39, p. 100366, 2021.
[9] R. Zolfaghari and A. M. Rahmani, “Virtual machine consolidation in cloud computing systems: Challenges and future trends,” Wirel. Pers. Commun., vol. 115, no. 3, pp. 2289–2326, 2020.
[10] M.-H. Malekloo, N. Kara, and M. El Barachi, “An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments,” Sustain. Comput. Informatics Syst., vol. 17, pp. 9–24, 2018.
[11] U. Arshad, M. Aleem, G. Srivastava, and J. C.-W. Lin, “Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers,” Renew. Sustain. Energy Rev., vol. 167, p. 112782, 2022.
[12] H. F. Farimani, D. Bahrepour, and S. R. K. Tabbakh, “Reallocation of virtual machines to cloud data centers to reduce service level agreement violation and energy consumption using the FMT method,” J. Inf. Syst. Telecommun., vol. 4, no. 28, p. 316, 2020.
[13] S. Mustafa, K. Bilal, S. U. R. Malik, and S. A. Madani, “SLA-aware energy efficient resource management for cloud environments,” IEEE Access, vol. 6, pp. 15004–15020, 2018.
[14] D. Dabhi and D. Thakor, “Energy and SLA-Aware VM Placement Policy for VM Consolidation Process in Cloud Data Centers,” in Sustainable Technology and Advanced Computing in Electrical Engineering: Proceedings of ICSTACE 2021, Springer, 2022, pp. 351–365.
[15] K. M, “Energy-Aware Virtual Machine Consolidation Algorithm for Enhanced QoS in Data Centers,” Int. Sci. J. Eng. Manag., vol. 03, no. 04, pp. 1–9, Apr. 2024.
[16] U. Khalid, S. Ahmad, B. Chang, M. Nisar, J. Cha, and E. Munir, Energy Optimization in Cloud Computing Environmentthrough Virtual Machine Consolidation. 2023.
[17] A. Ali and T. T. Tin, “Unleashing the Power of Consolidate Cloud Computing: Secure and Energy-Efficient Virtual Machines at Your Service,” 2023.
[18] R. Shaw, E. Howley, and E. Barrett, “Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers,” Inf. Syst., vol. 107, p. 101722, 2022.
[19] D. Alsadie and M. Alsulami, “Efficient Resource Management in Cloud Environments: A Modified Feeding Birds Algorithm for VM Consolidation,” Mathematics, vol. 12, no. 12, p. 1845, Jun. 2024.
[20] R. P. Patel and H. B. Bhadka, “Energy-Aware VMs Consolidation Computing Frameworks’ of Data Center in Cloud Computing Environment,” J. Sci. Technol., vol. 7, no. 1, pp. 82–91, 2022.
[21] N. Manikandan, P. Divya, and S. Janani, “BWFSO: hybrid Black-widow and Fish swarm optimization Algorithm for resource allocation and task scheduling in cloud computing,” Mater. Today Proc., vol. 62, pp. 4903–4908, 2022.
[22] A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers,” Concurr. Comput. Pract. Exp., vol. 24, no. 13, pp. 1397–1420, 2012.
[23] Ç. İ. Acı and H. Gülcan, “A modified dragonfly optimization algorithm for single‐and multiobjective problems using Brownian motion,” Comput. Intell. Neurosci., vol. 2019, no. 1, p. 6871298, 2019.
[24] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Softw. Pract. Exp., vol. 41, no. 1, pp. 23–50, 2011.
[25] K. Park and V. S. Pai, “CoMon: a mostly-scalable monitoring system for PlanetLab,” ACM SIGOPS Oper. Syst. Rev., vol. 40, no. 1, pp. 65–74, 2006.
