Abstract
Energy optimization for cloud computing services has gained a considerable momentum over the recent years. Unfortunately, minimizing energy consumption of cloud services has its own unique research problems and challenges. More specifically, it is difficult to select suitable servers for cloud service systems to minimize energy consumption due to the heterogeneity of servers in cloud centers. In this paper, the energy minimization problem is considered for cloud systems with stochastic service requests and system availability constraints where the stochastic cloud service requests are constrained by deadlines. An energy minimization algorithm is proposed to select the most suitable servers to achieve the energy efficiency of cloud services. Our intensive experimental studies based on both simulated and real cloud instances show the proposed algorithm is much more effective with acceptable CPU utilization, saving up to 61.95% energy consumption, than the existing algorithms.
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References
Bilal, K., Fayyaz, A., Khan, S.U., Usman, S.: Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis. Cluster Comput. 18(2), 865–888 (2015). https://doi.org/10.1007/s10586-015-0437-9
Chen, S., Wang, Y., Pedram, M.: A semi-Markovian decision process based control method for offloading tasks from mobile devices to the cloud. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 2885–2890. IEEE (2013)
Entezari-Maleki, R., Sousa, L., Movaghar, A.: Performance and power modeling and evaluation of virtualized servers in IaaS clouds. Inf. Sci. 394–395, 106–122 (2017)
Gerards, M.E.T., Hurink, J.L., Hölzenspies, P.K.F.: A survey of offline algorithms for energy minimization under deadline constraints. J. Sched. 19(1), 3–19 (2016). https://doi.org/10.1007/s10951-015-0463-8
Gross, D., Harris, C.M.: Fundamentals of Queueing Theory. Wiley, New York (2008)
Khazaei, H., Ic, J., Ic, V.B., Mohammadi, N.B.: Modeling the performance of heterogeneous IaaS cloud centers. In: IEEE International Conference on Distributed Computing Systems Workshops, pp. 232–237. Philadelphia, PA, USA (2013)
Li, K.: Optimal power allocation among multiple heterogeneous servers in a data center. Sustain. Comput. Inf. Syst. 2, 13–22 (2012)
Li, K.: Improving multicore server performance and reducing energy consumption by workload dependent dynamic power management. IEEE Trans. Cloud Comput. 4(2), 122–137 (2016)
Mei, J., Li, K., Li, K.: Customer-satisfaction-aware optimal multiserver configuration for profit maximization in cloud computing. IEEE Trans. Sustain. Comput. 2(1), 17–29 (2017)
Mitrani, I.: Managing performance and power consumption in a server farm. Ann. Oper. Res. 202(1), 121–134 (2013)
Shehabi, A., et al.: United states data center energy usage report. Technical report, Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States) (2016)
Tarello, A., Sun, J., Zafer, M., Modiano, E.: Minimum energy transmission scheduling subject to deadline constraints. In: Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2005), pp. 67–76. IEEE (2005)
Tijms, H.C.: A First Course in Stochastic Models. Wiley, Amsterdam (2004)
Tirdad, A., Grassmann, W.K., Tavakoli, J.: Optimal policies of M(t)/M/C/C queues with two different levels of servers. Eur. J. Oper. Res. 249(3), 1124–1130 (2016)
Wang, S., Li, X., Ruiz, R.: Performance analysis for heterogeneous cloud servers using queueing theory. IEEE Trans. Comput. 69(4), 563–576 (2020)
Wu, C.M., Chang, R.S., Chan, H.Y.: A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Gener. Comput. Syst. 37, 141–147 (2014)
Zhai, B., Blaauw, D., Sylvester, D., Flautner, K.: Theoretical and practical limits of dynamic voltage scaling. In: Design Automation Conference. Proceedings, pp. 868–873 (2004)
Zheng, X., Yu, C.: Markov model based power management in server clusters. In: IEEE/ACM Intl Conference on Green Computing and Communications and International Conference on Cyber, Physical and Social Computing (2010)
Zhou, Z., et al.: Minimizing SLA violation and power consumption in cloud data centers using adaptive energy-aware algorithms. Future Gener. Comput. Syst. 86, 836–850 (2018)
Zomaya, A.Y., Lee, Y.C.: Energy-Efficient Distributed Computing Systems. Wiley, New York (2012)
Cong, X., Zi, L., Shuang, K.: Energy-aware and location-constrained virtual network embedding in enterprise network. In: Liu, X., et al. (eds.) ICSOC 2018. LNCS, vol. 11434, pp. 41–52. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17642-6_4
Xu, M., Buyya, R.: Energy efficient scheduling of application components via brownout and approximate Markov decision process. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 206–220. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69035-3_14
Sieminski, A., et al.: International energy outlook. Energy Inf. Admin. (EIA) 18 (2014)
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Wang, S., Sheng, Q.Z., Li, X., Mahmood, A., Zhang, Y. (2020). Energy Minimization for Cloud Services with Stochastic Requests. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_11
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