Abstract
The energy consumption of under-utilized resources, particularly in a cloud environment, accounts for a substantial amount of the actual energy use. Inherently, a resource allocation strategy that takes into account resource utilization would lead to a better energy efficiency; this, in clouds, extends further with virtualization technologies in that tasks can be easily consolidated. Task consolidation is an effective method to increase resource utilization and in turn reduces energy consumption. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization. This encouraging fact further highlights the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power. There have been some notable efforts to reduce idle power draw, typically by putting computer resources into some form of sleep/power-saving mode. In this paper, we present two energy-conscious task consolidation heuristics, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption. Our heuristics assign each task to the resource on which the energy consumption for executing the task is explicitly or implicitly minimized without the performance degradation of that task. Based on our experimental results, our heuristics demonstrate their promising energy-saving capability.
Similar content being viewed by others
References
Parkhill D (1966) The challenge of the computer utility. Addison-Wesley Educational, Reading
Koomey JG (2007) Estimating total power consumption by servers in the U.S. and the world. Lawrence Berkeley National Laboratory, Stanford University
Koch G (2005) Discovering multi-core: Extending the benefits of Moore’s law. Technology@Intel Magazine, (http://www.intel.com/technology/magazine/computing/multi-core-0705.pdf)
Barroso L, Holzle U (2007) The case for energy-proportional computing. IEEE Comput
Bohrer P, Elnozahy E, Keller T, Kistler M, Lefurgy C, Rajamony R (2002) The case for power management in web servers. Power Aware Comput 261–289
Fan X, Weber X-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proc 34th annual international symposium on computer architecture (ISCA ’07), 2007, pp 13–23
Lefurgy C, Wang X, Ware M (2007) Server-level power control. In: Proc IEEE international conference on autonomic computing, Jan 2007
Meisner D, Gold BT, Wenisch TF (2009) PowerNap: eliminating server idle power. In: Proc 14th international conference on architectural support for programming languages and operating systems (ASPLOS ’09), 2009, pp 205–216
Microsoft Inc (2009) Explore the features: performance. http://www.microsoft.com/windows/windows-vista/features/performance.aspx
Venkatachalam V, Franz M (2005) Power reduction techniques for microprocessor systems. ACM Comput Surv 37(3):195–237
Lee YC, Zomaya AY (2009) Minimizing energy consumption for precedence-constrained applications using dynamic voltage scaling. In: Proc the international symposium on cluster computing and the grid (CCGRID ’09), 2009, pp 92–99
Kim KH, Buyya R, Kim J (2007) Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In: Proc seventh IEEE international symposium on cluster computing and the grid (CCGrid ’07), 2007, pp 541–548
Zhu D, Melhem R, Childers BR (2003) Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems. IEEE Trans Parallel Distrib Syst 14(7):686–700
Ge R, Feng X, Cameron KW (2005) Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters. In: Proc the ACM/IEEE conference on supercomputing (SC ’05), 2005, pp 34–44
Chen JJ, Kuo TW (2005) Multiprocessor energy-efficient scheduling for real-time tasks with different power characteristics. In: Proc international conference on parallel processing (ICPP ’05), 2005, pp 13–20
Moore J, Chase J, Ranganathan P, Sharma R (2005) Making scheduling cool: temperature-aware workload placement in data centers. In: Proc USENIX annual technical conference
Tang Q, Gupta SK, Varsamopoulos G (2008) Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans Parallel Distrib Syst 19(11):1458–1472
Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Proc USENIX workshop on power aware computing and systems in conjunction with OSDI, 2008, pp 1–5
Song Y, Zhang Y, Sun Y, Shi W (2009) Utility analysis for internet-oriented server consolidation in VM-based data centers. In: Proc IEEE international conference on cluster computing (Cluster ’09), 2009
Torres J, Carrera D, Hogan K, Gavalda R, Beltran V, Poggi N (2008) Reducing wasted resources to help achieve green data centers. In: Proc 4th workshop on high-performance, power-aware computing (HPPAC ’08), 2008
Nathuji R, Schwan K (2007) VirtualPower: coordinated power management in virtualized enterprise systems. In: Proc twenty-first ACM SIGOPS symposium on operating systems principles (SOSP ’07), 2007, pp 265–278
Kuroda T, Suzuki K, Mita S, Fujita T, Yamane F, Sano F, Chiba A, Watanabe Y, Matsuda K, Maeda T, Sakurai T, Furuyama T (1998) Variable supply–voltage scheme for low–power high–speed CMOS digital design. IEEE J Solid-State Circuits 33(3):454–462
Subrata R, Zomaya AY, Landfeldt B (2010) Cooperative power-aware scheduling in grid computing environments. J Parallel Distrib Comput 70(2):84–91
Khan SU, Ahmad I (2009) A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids. IEEE Trans Parallel Distrib Syst 21(4):537–553
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, Y.C., Zomaya, A.Y. Energy efficient utilization of resources in cloud computing systems. J Supercomput 60, 268–280 (2012). https://doi.org/10.1007/s11227-010-0421-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-010-0421-3