Abstract
Cloud computing is an emerging computing paradigm in which “Everything is as a Service”, including the provision of virtualized computing infrastructures (known as Infrastructure-as-a-Service modality) hosted on the physical infrastructure, owned by an infrastructure provider. The goal of this infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with its customers and, at the same time, by lowering infrastructure costs among which energy consumption plays a major role. In this paper, we propose a framework able to automatically manage resources of cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.
This work was supported in part by the Italian Research Ministry under the PRIN 2008 Energy eFFIcient teChnologIEs for the Networks of Tomorrow (EFFICIENT) project.
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References
Weiss, A.: Computing in the clouds. netWorker 11(4), 16–25 (2007)
ENERGY STAR Program: Report to congress on server and data center energy efficiency. Technical report, U.S. EPA (August 2007)
Guazzone, M., et al.: Energy-efficient resource management for cloud computing infrastructures. In: Proc. of the 3rd IEEE Int. Conf. on Cloud Computing Technology and Science (CloudCom 2011) (2011)
Banks, J., et al.: Discrete-Event System Simulation, 5th edn. Prentice Hall (2010)
Guazzone, M., et al.: Exploiting VM migration for the automated power and performance management of green cloud computing systems. Technical Report TR-INF-2012-04-02-UNIPMN, University of Piemonte Orientale (April 2012)
Lee, J., et al. (eds.): Mixed Integer Nonlinear Programming. The IMA Volumes in Mathematics and its Applications, vol. 154. Springer Science+Business Media, LLC (2012)
Jeroslow, R.: There cannot be any algorithm for integer programming with quadratic constraints. Oper. Res. 21(1), 221–224 (1973)
Yang, L.T., et al.: Cross-platform performance prediction of parallel applications using partial execution. In: Proc. of the 2005 ACM/IEEE Conference on Supercomputing, SC 2005 (2005)
Wood, T., et al.: Profiling and Modeling Resource Usage of Virtualized Applications. In: Issarny, V., Schantz, R. (eds.) Middleware 2008. LNCS, vol. 5346, pp. 366–387. Springer, Heidelberg (2008)
Beloglazov, A., et al.: A taxonomy and survey of energy-efficient data centers and cloud computing systems. In: Zelkowitz, M.V. (ed.) Advances in Computers, vol. 82, pp. 47–111. Elsevier (2011)
Fan, X., et al.: Power provisioning for a warehouse-sized computer. In: Proc. of the 34th Int. Symp. on Computer Architecture (ISCA 2007), pp. 13–23 (2007)
Rivoire, S., et al.: A comparison of high-level full-system power models. In: Proc. of the 2008 USENIX Conf. on Power Aware Computing and Systems (HotPower 2008), pp. 1–5 (2008)
Standard Performance Evaluation Corporation: SPECpower_ssj2008 benchmark, http://www.spec.org/power_ssj2008
Le-Ngoc, T., et al.: A Pareto-modulated Poisson process (PMPP) model for long-range dependent traffic. Comput. Comm. 23(2), 123–132 (2000)
Fischer, W., et al.: The Markov-modulated Poisson Process (MMPP) cookbook. Perform. Eval. 18(2), 149–171 (1993)
Mi, N., et al.: Injecting realistic burstiness to a traditional client-server benchmark. In: Proc. of the 6th IEEE Int. Conf. on Autonomic Computing (ICAC 2009), pp. 149–158 (2009)
Beloglazov, A., et al.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurrency Comput. Pract. Ex. (accepted for publication)
Gandhi, A., et al.: Minimizing data center SLA violations and power consumption via hybrid resource provisioning. In: Proc. of the 2nd Int. Green Computing Conf., IGCC 2010 (2011)
Kusic, D., et al.: Combined power and performance management of virtualized computing environments serving session-based workloads. IEEE Trans. on Netw. and Serv. Manag. 8(3), 245–258 (2011)
Camacho, E.F., et al.: Model Predictive Control, 2nd edn. Springer (2004)
Xiong, P., et al.: Economical and robust provisioning of n-tier cloud workloads: A multi-level control approach. In: Proc. of the 31st Int. Conf. on Distributed Computing Systems (ICDCS 2011), pp. 571–580 (2011)
Wang, X., et al.: Coordinating power control and performance management for virtualized server clusters. IEEE Trans. Parallel Distrib. Syst. 22(2), 245–259 (2011)
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Guazzone, M., Anglano, C., Canonico, M. (2012). Exploiting VM Migration for the Automated Power and Performance Management of Green Cloud Computing Systems. In: Huusko, J., de Meer, H., Klingert, S., Somov, A. (eds) Energy Efficient Data Centers. E2DC 2012. Lecture Notes in Computer Science, vol 7396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33645-4_8
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DOI: https://doi.org/10.1007/978-3-642-33645-4_8
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