Abstract
Virtual machine consolidation (VMC) is a technology that aggregates virtual machines distributed on multiple physical machines into a small number of physical machines to improve resource utilization and energy efficiency of data center. However, excessive virtual machine aggregation and migration can also have a significant negative impact on performance. In this paper, an algorithm named ELVMC with multiple resource prediction is proposed for optimal virtual machine consolidation. It applies a modified Best-Fit Decreasing (BFD) algorithm for resource optimization at both overloaded hosts and underloaded hosts with consideration of load balancing. Different from current research, ELVMC aims to obtain an optimal virtual machine (VM) placement during each consolidation process by simultaneously optimizing multiple system performance metrics in terms of energy consumption, VM migrations and QoS guarantees while keeping the load balanced. Simulation results show that ELVMC is superior to the state of the arts, including the traditional BFD and SABFD-HS algorithms as well as recent research VMCUP-M and MUC-MBFD.
Supported by the National Natural Science Foundation of China under Grant No. 61662054, 61262082, Inner Mongolia Colleges and Universities of Young Technology Talent Support Program under Grant No. NJYT-19-A02, the Major Project of Inner Mongolia Natural Science Foundation: Research on Key Technologies of Cloud Support for Big Data Intelligent Analysis under Grant No. 2019ZD15, Natural Science Foundation of Inner Mongolia under Grand No. 2015MS0608, Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering, and Inner Mongolia Application Technology Research and Development Funding Project “Mutual Creation Service Platform Research and Development Based on Service Optimizing and Operation Integrating”.
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References
Liu, Z., Tong, W., Gong, Z.X., Liu, J., Yue, H., Guo, S.: Cloud computing model without resource management center. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2011)
Gao, Y., Guan, H., Qi, Z., Yang, H., Liang, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)
Chiu, D., Stewart, C., Mcmanus, B.: Electric grid balancing through lowcost workload migration. ACM SIGMETRICS Perform. Eval. Rev. 40(3), 48–52 (2012)
Birke, R., Chen, L.Y., Smirni, E.: Data centers in the cloud: a large scale performance study. In: IEEE International Conference on Cloud Computing (2012)
Hameed, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016). https://doi.org/10.1007/s00607-014-0407-8
Amekraz, Z., Hadi, M.Y.: An adaptive workload prediction strategy for non-gaussian cloud service using ARMA model with higher order statistics. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (2018)
Zhang, Z., Xiao, L., Li, Y., Li, R.: A VM-based resource management method using statistics. In: IEEE International Conference on Parallel and Distributed Systems (2012)
Ishak, S., Al-Deek, H.: Performance evaluation of short-term time-series traffic prediction model. J. Transp. Eng. 128(6), 490–498 (2002)
Prevost, J.J., Nagothu, K.M., Kelley, B., Mo, J.: Prediction of cloud data center networks loads using stochastic and neural models. In: International Conference on System of Systems Engineering (2011)
Alsadie, D., Tari, Z., Alzahrani, E.J.: Online VM consolidation in cloud environments. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 137–145. IEEE (2019)
Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, H.: Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. PP(99), 1 (2016)
Hui, W., Tianfield, H.: Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access 6(99), 15259–15273 (2018)
Nguyen, T.H., Di Francesco, M., Yla-Jaaski, A.: Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Serv. Comput. 13, 186–199 (2017)
Moghaddam, S.M., OâSullivan, M., Walker, C., Piraghaj, S.F., Unsworth, C.P.: Embedding individualized machine learning prediction models for energy efficient VM consolidation within cloud data centers. Future Gener. Comput. Syst. 106, 221–233 (2020)
Min, Y.L., Rawson, F., Bletsch, T., Freeh, V.W.: PADD: Power aware domain distribution. In: IEEE International Conference on Distributed Computing Systems (2009)
Raju, R., Amudhavel, J., Kannan, N., Monisha, M.: A bio inspired energy-aware multi objective chiropteran algorithm (EAMOCA) for hybrid cloud computing environment. In: International Conference on Green Computing Communication and Electrical Engineering (2014)
Murtazaev, A., Oh, S.: Sercon: Server consolidation algorithm using live migration of virtual machines for green computing. IETE Tech. Rev. 28(3), 212 (2011)
Alsadie, D., Tari, Z., Alzahrani, E.J., Zomaya, A.Y.: Life: a predictive approach for VM placement in cloud environments. In: IEEE International Symposium on Network Computing and Applications (2017)
Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y.: Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans. Serv. Comput. PP(99), 1 (2016)
Pacheco-Sanchez, S., Casale, G., Scotney, B., Mcclean, S., Parr, G., Dawson, S.: Markovian workload characterization for QoS prediction in the cloud. In: IEEE International Conference on Cloud Computing (2011)
Hieu, N.T., Francesco, M.D., Yla-Jaaski, A.: Virtual machine consolidation with usage prediction for energy-efficient cloud data centers. In: IEEE International Conference on Cloud Computing (2015)
Calheiros, R.N., Masoumi, E., Ranjan, R., Buyya, R.: Workload prediction using ARIMA model and its impact on cloud application’ QoS. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015). 2
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exper. 24(13), 1397–1420 (2012)
Sayadnavard, M.H., Haghighat, A.T., Rahmani, A.M.: A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers. J. Supercomput. 75(4), 2126–2147 (2019)
Thiam, C., Thiam, F.: Energy efficient cloud data center using dynamic virtual machine consolidation algorithm. In: Abramowicz, W., Corchuelo, R. (eds.) BIS 2019. LNBIP, vol. 353, pp. 514–525. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20485-3_40
Liu, Y., Sun, X., Wei, W., Jing, W.: Enhancing energy-efficient and QoS dynamic virtual machine consolidation method in cloud environment. IEEE Access 6(99), 1 (2018)
Tian, W., et al.: On minimizing total energy consumption in the scheduling of virtual machine reservations. J. Netw. Comput. Appl. 113, 64–74 (2018)
Beloglazov, A.: Energy-efficient management of virtual machines in data centers for cloud computing. Department of Computing and Information Systems (2013)
Liu, Z., Cho, S.: Characterizing machines and workloads on a google cluster. In: International Conference on Parallel Processing Workshops (2012)
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Zhao, Dm., Zhou, Jt., Yu, S. (2020). ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_5
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