Toward an Enhanced Dynamic VM Consolidation Approach for Cloud Datacenters Using Continuous Time Markov Chain
Subject Areas : Cloud computingMonireh Hosseini Sayadnavard 1 , Abolfazl Toroghi Haghighat 2
1 - Science and Research Branch Islamic Azad University, Tehran
2 - Qazvin Branch Islamic Azad University
Keywords: Cloud Computing, , VM Consolidation, , Energy Efficiency, , Reliability, , Markov Chain,
Abstract :
Dynamic Virtual Machine (VM) consolidation is an effective manner to reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers that guarantees efficient power consumption while maintaining the quality of service requirements. Reducing the number of active PMs using VM live migration leads to prevent inefficient usage of resources. However, high frequency of VM consolidation has a negative effect on the system reliability and we need to deal with the trade-off between energy consumption and system reliability. In recent years many research work has been done to optimize energy management using power management techniques. Although these methods are very efficient from the point of view of energy management, but they ignore the negative impact on the system reliability. In this paper, a novel approach is proposed to achieve a reliable VM consolidation method. In this way, a Markov chain model is designed to determine the reliability of PMs and then it has been prioritized PMs based on their CPU utilization level and reliability status. Two algorithms are presented to determining source and destination servers. The efficiency of our proposed approach is validated by conducting extensive simulations. The results of the evaluation clearly show that the proposed approach significantly improve energy consumption while avoiding the inefficient VM migrations.
[1] M. Armbrust et al., "A view of cloud computing," Communications of the ACM, vol. 53, no. 4, pp. 50-58, 2010.
[2] M. Mills, "The cloud begins with coal. big data, big networks, big infrastructure, and big power. an overview of the electricity used by the digital ecosystem," ed, 2013.
[3] 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.
[4] A. Varasteh and M. Goudarzi, "Server consolidation techniques in virtualized data centers: A survey," IEEE Systems Journal, vol. 11, no. 2, pp. 772-783, 2017.
[5] A. Beloglazov and R. Buyya, "Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 7, pp. 1366-1379, 2013.
[6] A. Beloglazov, "Energy-efficient management of virtual machines in data centers for cloud computing," 2013.
[7] Y. Sharma, B. Javadi, W. Si, and D. Sun, "Reliability and energy efficiency in cloud computing systems: Survey and taxonomy," Journal of Network and Computer Applications, vol. 74, pp. 66-85, 2016.
[8] W. Deng, F. Liu, H. Jin, X. Liao, and H. Liu, "Reliability‐aware server consolidation for balancing energy‐lifetime tradeoff in virtualized cloud datacenters," International Journal of Communication Systems, vol. 27, no. 4, pp. 623-642, 2014.
[9] A. Varasteh, F. Tashtarian, and M. Goudarzi, "On Reliability-Aware Server Consolidation in Cloud Datacenters," arXiv preprint arXiv:1709.00411, 2017.
[10] L. Grit, D. Irwin, A. Yumerefendi, and J. Chase, "Virtual machine hosting for networked clusters: Building the foundations for autonomic orchestration," in Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing, 2006, p. 7: IEEE Computer Society.
[11] B. Speitkamp and M. Bichler, "A mathematical programming approach for server consolidation problems in virtualized data centers," IEEE Transactions on services computing, vol. 3, no. 4, pp. 266-278, 2010.
[12] A. Beloglazov, J. Abawajy, and R. Buyya, "Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing," Future generation computer systems, vol. 28, no. 5, pp. 755-768, 2012.
[13] A. Beloglazov and R. Buyya, "Energy efficient resource management in virtualized cloud data centers," in Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing, 2010, pp. 826-831: IEEE Computer Society.
[14] S. Esfandiarpoor, A. Pahlavan, and M. Goudarzi, "Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing," Computers & Electrical Engineering, vol. 42, pp. 74-89, 2015.
[15] S. Zhang, Z. Qian, Z. Luo, J. Wu, and S. Lu, "Burstiness-aware resource reservation for server consolidation in computing clouds," IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 4, pp. 964-977, 2016.
[16] H. Shen and L. Chen, "Compvm: A complementary vm allocation mechanism for cloud systems," IEEE/ACM Transactions on Networking (TON), vol. 26, no. 3, pp. 1348-1361, 2018.
[17] E. Arianyan, 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, vol. 78, pp. 43-61, 2017.
[18] K. S. Rao and P. S. Thilagam, "Heuristics based server consolidation with residual resource defragmentation in cloud data centers," Future Generation Computer Systems, vol. 50, pp. 87-98, 2015.
[19] A. Ponraj, "Optimistic virtual machine placement in cloud data centers using queuing approach," Future Generation Computer Systems, vol. 93, pp. 338-344, 2019.
[20] Z. Li, C. Yan, X. Yu, and N. Yu, "Bayesian network-based virtual machines consolidation method," Future Generation Computer Systems, vol. 69, pp. 75-87, 2017.
[21] H. Khani, A. Latifi, N. Yazdani, and S. Mohammadi, "Distributed consolidation of virtual machines for power efficiency in heterogeneous cloud data centers," Computers & Electrical Engineering, vol. 47, pp. 173-185, 2015.
[22] T. Mahdhi and H. Mezni, "A prediction-Based VM consolidation approach in IaaS Cloud Data Centers," Journal of Systems and Software, vol. 146, pp. 263-285, 2018.
[23] F. Farahnakian et al., "Using ant colony system to consolidate VMs for green cloud computing," IEEE Transactions on Services Computing, vol. 8, no. 2, pp. 187-198, 2015.
[24] H. Mi, H. Wang, G. Yin, Y. Zhou, D. Shi, and L. Yuan, "Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers," in Services Computing (SCC), 2010 IEEE International Conference on, 2010, pp. 514-521: IEEE.
[25] H. Li, G. Zhu, C. Cui, H. Tang, Y. Dou, and C. He, "Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing," Computing, vol. 98, no. 3, pp. 303-317, 2016.
[26] H. Zhao, J. Wang, F. Liu, Q. Wang, W. Zhang, and Q. Zheng, "Power-aware and performance-guaranteed virtual machine placement in the cloud," IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 6, pp. 1385-1400, 2018.
[27] B. Wei, C. Lin, and X. Kong, "Dependability modeling and analysis for the virtual data center of cloud computing," in High Performance Computing and Communications (HPCC), 2011 IEEE 13th International Conference on, 2011, pp. 784-789: IEEE.
[28] N. B. Fuqua, "The applicability of markov analysis methods to reliability, maintainability, and safety," Selected Topic in Assurance Related Technologies (START), vol. 2, no. 10, pp. 1-8, 2003.
[29] K. S. Trivedi, Probability & statistics with reliability, queuing and computer science applications. John Wiley & Sons, 2008. [30] A. Goyal, S. S. Lavenberg, and K. S. Trivedi, "Probabilistic modeling of computer system availability," Annals of Operations Research, vol. 8, no. 1, pp. 285-306, 1987.
[31] J. F. Meyer, "Closed-form solutions of performability," IEEE Transactions on Computers, no. 7, pp. 648-657, 1982.
[32] F. Machida, D. S. Kim, and K. S. Trivedi, "Modeling and analysis of software rejuvenation in a server virtualized system with live VM migration," Performance Evaluation, vol. 70, no. 3, pp. 212-230, 2013.
[33] J. K. Ghosh, "Introduction to Modeling and Analysis of Stochastic Systems, by VG Kulkarni," International Statistical Review, vol. 80, no. 3, pp. 487-487, 2012. [34] 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," Concurrency and Computation: Practice and Experience, vol. 24, no. 13, pp. 1397-1420, 2012.
[35] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms," Software: Practice and experience, vol. 41, no. 1, pp. 23-50, 2011.
[36] K. Park and V. S. Pai, "CoMon: a mostly-scalable monitoring system for PlanetLab," ACM SIGOPS Operating Systems Review, vol. 40, no. 1, pp. 65-74, 2006.
[37] R. d. S. Matos, P. R. Maciel, F. Machida, D. S. Kim, and K. S. Trivedi, "Sensitivity analysis of server virtualized system availability," IEEE Transactions on Reliability, vol. 61, no. 4, pp. 994-1006, 2012.
