Allocating workload to minimize the power consumption of data centers

Abstract

Reducing the power consumption has become one of the most important challenges in designing modern data centers due to the explosive growth of data. The traditional approaches employed to decrease the power consumption normally do not consider the power of IT devices and the power of cooling system simultaneously. In contrast to existing works, this paper proposes a power model which can minimize the overall power consumption of data centers by balancing the computing power and cooling power. Furthermore, an enhanced genetic algorithm (EGA) is designed to explore the solution space of the power model since the model is a liner programming problem. However, EGA is computing intensive and the performance gradually decreases with the growth of the problem size. Therefore, heuristic greedy sequence (HGS) is proposed to simplify the calculation by leveraging the nature of greed. In contrast to EGA, HGS can determine the workload allocation of a specific data center layout with only one calculation. Experimental results demonstrate that both the EGA and HGS can significantly reduce the power consumption of data centers in contrast to the random algorithm. Additionally, HGS significantly outperforms EGA in terms of the continuity of workload allocation and execution performance.

Keywords

data center / energy conservation / workload allocation / power model

References

[1] Hua Y, Liu X, Feng D. Data similarity-aware computation infrastructure for the cloud. IEEE Transactions on Computers, 2014, 63(1): 3–16

[2] Van Heddeghem W, Lambert S, Lannoo B, Colle D, Pickavet M, Demeester P. Trends in worldwide ICT electricity consumption from 2007 to 2012. Computer Communications, 2014, 50: 64–76

[3] Barroso L A, Hölzle U. The case for energy-proportional computing. Computer, 2007, 40(12): 33–37

[4] Weiser M, Welch B, Demers A, Shenker S. Scheduling for reduced cpu energy. Mobile Computing, 1996, 449–471

[5] Berl A, Gelenbe E, Di Girolamo M, Giuliani G, De Meer H, Dang M Q, Pentikousis K. Energy-efficient cloud computing. The Computer Journal, 2010, 53(7): 1045–1051

[6] Rong H G, Zhang H M, Xiao S, Li C B, Hu C H. Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 2016, 58: 674–691

[7] Sawyer R. Calculating total power requirements for data centers. White Paper, American Power Conversion, 2004

[8] Moore J D, Chase J S, Ranganathan P, Sharma R K. Making scheduling “cool”: temperature-aware workload placement in data centers. In: Proceedings of USENIX Annual Technical Conference, General Track. 2005, 61–75

[9] Tang Q, Gupta S K S, Varsamopoulos G. Energy-efficient thermalaware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(11): 1458–1472

[10] Shamalizadeh H, Almeida L, Wan S, Amaral P, Fu S, Prabh S. Optimized thermal-aware workload distribution considering allocation constraints in data centers. In: Proceedings of IEEE Green Computing and Communications. 2013, 208–214

[11] Kaur T, Chana I. Energy efficiency techniques in cloud computing: a survey and taxonomy. ACM Computing Surveys (CSUR), 2015, 48(2): 22

[12] Chaudhry M T, Ling T C, Manzoor A, Hussain S A, Kim J. Thermalaware scheduling in green data centers. ACM Computing Surveys (CSUR), 2015, 47(3): 39

[13] Pinheiro E, Bianchini R, Carrera E, Heath T. Dynamic cluster reconfiguration for power and performance. In: Proceedings of Workshop on Compilers and Operating Systems for Lowpower. 2003, 75–93

[14] Verma A, Ahuja P, Neogi A. Power-aware dynamic placement of hpc applications. In: Proceedings of the 22nd Annual International Conference on Supercomputing. 2008, 175–184

[15] Zhang L W, Deng Y H, Zhu W H, Zhou J P, Wang F. Skewly replicating hot data to construct a power-efficient storage cluster. Journal of Network and Computer Applications, 2015, 50: 168–179

[16] Deng Y H. What is the future of disk drives, death or rebirth? ACM Computing Surveys (CSUR), 2011, 43(3): 23

[17] Lin R H, Deng Y H, Yang L Y. Conserving cooling and computing power by distributing workloads in data centers. In: Proceedings of the 13th ACM International Conference on Computing Frontiers. 2016

[18] Kansal A, Zhao F. Fine-grained energy profiling for power-aware application design. ACM SIGMETRICS Performance Evaluation Review, 2008, 36(2): 26–31

[19] Deng Y H, Hu Y, Meng X H, Zhu Y F, Zhang Z, Han J Z. Predictively booting nodes to minimize performance degradation of a power-aware web cluster. Cluster Computing, 2014, 17(4): 1309–1322

[20] Kantarci B, Foschini L, Corradi A, Mouftah H T. Design of energyefficient cloud systems via network and resource virtualization. International Journal of Network Management, 2015, 25(2): 75–94

[21] Moore J, Chase J S, Ranganathan P. Weatherman: automated, online and predictive thermal mapping and management for data centers. In: Proceedings of IEEE International Conference on Autonomic Computing. 2006, 155–164

[22] Marshall L, Bemis P. Using CFD for data center design and analysis. Applied Math Modeling White Paper, 2011

[23] Sharma R K, Bash C E, Patel C D. Dimensionless parameters for evaluation of thermal design and performance of large-scale data centers. In: Proceedings of the 8th ASME/AIAA Joint Thermophysics and Heat Transfer Conference. 2002

[24] Tang Q, Mukherjee T, Gupta S K, Cayton P. Sensor-based fast thermal evaluation model for energy efficient high-performance datacenters. In: Proceedings of the 4th International Conference on Intelligent Sensing and Information Processing. 2006, 203–208

[25] Weiss B, Truong H L, Schott W, Scherer T, Lombriser C, Chevillat P. Wireless sensor network for continuously monitoring temperatures in data centers. IBM RZ, 2011

[26] Ahmad F, Vijaykumar T. Joint optimization of idle and cooling power in data centers while maintaining response time. ACM SIGPLAN Notices, 2010, 45(3): 243–256

[27] Lent R. Analysis of an energy proportional data center. Ad Hoc Networks, 2015, 25: 554–564

[28] Cupertino L, Da Costa G, Oleksiak A, Pia W, Pierson J M, Salom J, Siso L, Stolf P, Sun H Y, Zilio T. Energy-efficient, thermal-aware modeling and simulation of data centers: the CoolEmAll approach and evaluation results. Ad Hoc Networks, 2015, 25: 535–553

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg