Abstract
Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and satisfying quality of service (e.g. performance, resource availability on time for reservation requests). We consider resource needs in the context of virtualized data centers of a private cloud system, which provides resource leases in terms of virtual machines (VMs) for user applications. In this paper, we propose heuristics for scheduling VMs that address the above challenge. On performance evaluation, simulated results have shown a significant reduction on total energy consumption of our proposed algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling algorithm with the same fulfillment of performance requirements. We also discuss the improvement of energy saving when additionally using migration policies to the above mentioned algorithms.
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References
Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)
Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)
Fan, X., Weber, W.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput. Archit. News. 35, 13 (2007)
Feitelson, D.G., Rudolph, L., Schwiegelshohn, U.: Parallel job scheduling – a status report. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 1–16. Springer, Heidelberg (2005)
Goiri, Í., Nou, R., Berral, J., Guitart, J., Torres, J.: Energy-aware scheduling in virtualized datacenters. In: IEEE International Conference on Cluster Computing, CLUSTER 2010, Heraklion, pp. 58–67 (2010)
Panigrahy, R., Talwar, K., Uyeda, L., Wieder, U.: Heuristics for vector bin packing. Technical report, Microsoft Research (2011)
Quang-Hung, N., Thoai, N., Son, N.T.: Performance constraint and power-aware allocation for user requests in virtual computing lab. J. Sci. Technol. (Vietnam), 49(4A), 383–392 (2011)
von Laszewski, G., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: IEEE International Conference on Cluster Computing and Workshops, 2009, New Orleans, pp. 1–10 (2009). doi:10.1109/CLUSTR.2009.5289182
Sotomayor, B.: Provisioning Computational resources using virtual machines and leases. PhD Thesis submited to The University of Chicago, US (2010)
Sotomayor, B., Keahey, K., Foster, I.: Combining batch execution and leasing using virtual machines. In: Proceedings of the Eighteenth International Symposium on High Performance Distributed Computing (HPDC’08), Boston, 23–27 June 2008, pp. 87–96 (2008)
SPECpower ssj2008 results for HP ProLiant DL585 G5 (2.70 GHz, AMD Opteron 8384). http://bit.ly/JrkskF
SPECpower ssj2008 results for HP ProLiant DL785 G5 (2.30 GHz, AMD Opteron 8376 HE). http://bit.ly/K99RfD
The San Diego Supercomputer Center (SDSC) Blue Horizon log. http://bit.ly/JUQsiP
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Quang-Hung, N., Thoai, N., Thanh Son, N., Le, DK. (2014). Energy-Aware Lease Scheduling in Virtualized Data Centers. In: Bock, H., Hoang, X., Rannacher, R., Schlöder, J. (eds) Modeling, Simulation and Optimization of Complex Processes - HPSC 2012. Springer, Cham. https://doi.org/10.1007/978-3-319-09063-4_16
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DOI: https://doi.org/10.1007/978-3-319-09063-4_16
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