Abstract
High performance computing (HPC) clouds have become more popular for users to run their HPC applications on cloud infrastructures. Reduction in energy consumption (kWh) for these cloud systems is of high priority for any cloud provider. In this paper, we first study the energy-aware allocation of virtual machines (VMs) in HPC cloud systems along two dimensions: multi-dimensional resources and interval times of virtual machines. On the one hand, we present an example showing that using bin-packing heuristics (e.g. Best-Fit Decreasing) to minimize the number of physical servers could not lead to a minimum of total energy consumption. On the other hand, we find out that minimizing total energy consumption is equivalent to minimizing the sum of total completion time of all physical machines. Based on this finding, we propose the MinDFT-ST and MinDFT-FT algorithms to place the VMs onto the physical servers in such a way that minimizes the total completion times of all physical servers. Our simulation results show that MinDFT-ST and MinDFT-FT could reduce the total energy consumption by 22.4% and respectively 16.0% compared with state-of-the-art power-aware heuristics (such as power-aware best-fit decreasing) and vector bin-packing norm-based greedy algorithms (such as VBP-Norm-L1, VBP-Norm-L2, VBP-Norm-L30).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
The LPC log from the Parallel Workloads Archive, http://www.cs.huji.ac.il/labs/parallel/workload/l_lpc/LPC-EGEE-2004-1.2-cln.swf.gz (retrieved on January 30, 2014)
AWS - High Performance Computing - HPC Cloud Computing, http://aws.amazon.com/hpc/ (retrieved on August 31, 2014)
Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Comp. Syst. 28(5), 755–768 (2012)
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. Concurrency and Computation: Practice and Experience 24(13), 1397–1420 (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 Generation Comp. Syst. 25(6), 599–616 (2009)
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper. 41(1), 23–50 (2011)
Fan, X., Weber, W.D., Barroso, L.: Power provisioning for a warehouse-sized computer. In: ISCA, pp. 13–23 (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)
Garg, S.K., Yeo, C.S., Anandasivam, A., Buyya, R.: Energy-Efficient Scheduling of HPC Applications in Cloud Computing Environments. CoRR abs/0909.1146 (2009)
von Laszewski, G., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in dvfs-enabled clusters. In: CLUSTER, pp. 1–10 (2009)
Le, K., Bianchini, R., Zhang, J., Jaluria, Y., Meng, J., Nguyen, T.D.: Reducing electricity cost through virtual machine placement in high performance computing clouds. In: SC, p. 22 (2011)
Mämmelä, O., Majanen, M., Basmadjian, R., de Meer, H., Giesler, A., Homberg, W.: Energy-aware Job Scheduler for High-performance Computing (2012)
Mauch, V., Kunze, M., Hillenbrand, M.: High performance cloud computing. Future Generation Comp. Syst. 29(6), 1408–1416 (2013)
Panigrahy, R., Talwar, K., Uyeda, L., Wieder, U.: Heuristics for Vector Bin Packing. Tech. rep., Microsoft Research (2011)
Pham, T.V., Jamjoom, H., Jordan, K.E., Shae, Z.Y.: A service composition framework for market-oriented high performance computing cloud. In: HPDC, pp. 284–287 (2010)
Quang-Hung, N., Thoai, N., Son, N.: Epobf: Energy efficient allocation of virtual machines in high performance computing. J. Sci. Technol. Vietnamese Acad. Sci. Technol., Special on International Conference on Advanced Computing and Applications (ACOMP2013) 51(4B), 173–182 (2013)
Sotomayor, B.: Provisioning Computational Resources Using Virtual Machines and Leases. Ph.D. thesis, University of Chicago (2010)
Sotomayor, B., Keahey, K., Foster, I.T.: Combining batch execution and leasing using virtual machines. In: HPDC, pp. 87–96 (2008)
Takouna, I., Dawoud, W., Meinel, C.: Energy Efficient Scheduling of HPC-jobs on Virtualize Clusters using Host and VM Dynamic Configuration. Operating Systems Review 46(2), 19–27 (2012)
Viswanathan, H., Lee, E.K., Rodero, I., Pompili, D., Parashar, M., Gamell, M.: Energy-Aware Application-Centric VM Allocation for HPC Workloads. In: IPDPS Workshops, pp. 890–897 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Quang-Hung, N., Le, DK., Thoai, N., Son, N.T. (2014). Heuristics for Energy-Aware VM Allocation in HPC Clouds. In: Dang, T.K., Wagner, R., Neuhold, E., Takizawa, M., Küng, J., Thoai, N. (eds) Future Data and Security Engineering. FDSE 2014. Lecture Notes in Computer Science, vol 8860. Springer, Cham. https://doi.org/10.1007/978-3-319-12778-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-12778-1_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12777-4
Online ISBN: 978-3-319-12778-1
eBook Packages: Computer ScienceComputer Science (R0)