Qi Zhang
Joseph L. Hellerstein
ABSTRACT
Data centers have recently gained significant popularity as a cost-effective platform for hosting large-scale service applications. While large data centers enjoy economies of scale by amortizing initial capital investment over large number of machines, they also incur tremendous energy cost in terms of power distribution and cooling. An effective approach for saving energy in data centers is to adjust dynamically the data center capacity by turning off unused machines. However, this dynamic capacity provisioning problem is known to be challenging as it requires a careful understanding of the resource demand characteristics as well as considerations to various cost factors, including task scheduling delay, machine reconfiguration cost and electricity price fluctuation.
In this paper, we provide a control-theoretic solution to the dynamic capacity provisioning problem that minimizes the total energy cost while meeting the performance objective in terms of task scheduling delay. Specifically, we model this problem as a constrained discrete-time optimal control problem, and use Model Predictive Control (MPC) to find the optimal control policy. Through extensive analysis and simulation using real workload traces from Google's compute clusters, we show that our proposed framework can achieve significant reduction in energy cost, while maintaining an acceptable average scheduling delay for individual tasks.
- Energy star computers specification - feb. 14, 2012. http://www.energystar.gov/ia/partners/prod development/revisions/downloads/computer/ES Computers-Draft 1 Version 6.0 Specification.pdf.Google Scholar
- Eucalyptus community. http://open.eucalyptus.com/.Google Scholar
- Google cluster-usage traces: formatGoogle Scholar
- Googleclusterdata - traces of google workloads. http://code.google.com/p/googleclusterdata/.Google Scholar
- Technology research - Gartner Inc. http://www.gartner.com/it/page.jsp?id=1442113.Google Scholar
- U.S. Energy Information Administration (EIA). http://www.eia.gov/.Google Scholar
- Z. Abbasi, G. Varsamopoulos, and S. K. S. Gupta. Thermal aware server provisioning and workload distribution for Internet data centers. In Proceedings of the ACM International Symposium on High Performance Distributed Computing (HPDC), 2010. Google ScholarDigital Library
- C. Bash, C. Patel, and R. Sharma. Dynamic thermal management of air cooled data centers. In IEEE Intersociety Conference on the Thermal and Thermomechanical Phenomena in Electronics Systems (ITHERM), 2006.Google ScholarCross Ref
- G. E. P. Box, G. M. Jenkins, and G. C. Reinsel. Time Series Analysis, Forecasting, and Control. Prentice-Hall, third edition, 1994. Google ScholarDigital Library
- S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, USA, 2004. Google ScholarDigital Library
- G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao. Energy-aware server provisioning and load dispatching for connection-intensive Internet services. In USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2008. Google ScholarDigital Library
- J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 2008. Google ScholarDigital Library
- P. A. Dinda. Design, implementation, and performance of an extensible toolkit for resource prediction in distributed systems. IEEE Trans. Parallel Distrib. Syst., 17, February 2006. Google ScholarDigital Library
- X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Proceedings of the annual international symposium on Computer architecture (ISCA), 2007. Google ScholarDigital Library
- Y. Fu, C. Lu, and H. Wang. Robust control-theoretic thermal balancing for server clusters. In IEEE International Symposium on Parallel Distributed Processing (IPDPS), April 2010.Google ScholarCross Ref
- B. Guenter, N. Jain, and C. Williams. Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In IEEE INFOCOM, April 2011.Google ScholarCross Ref
- A. Gulati, A. Holler, M. Ji, G. Shanmuganathan, C. Waldspurger, and X. Zhu. VMware distributed resource management: Design, implementation, and lessons learned. In VMware Technical Journal, 2012.Google Scholar
- D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang. Power and performance management of virtualized computing environments via lookahead control. In Proceedings of the International Conference on Autonomic Computing (ICAC), 2008. Google ScholarDigital Library
- A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. Cutting the electric bill for Internet-scale systems. In ACM SIGCOMM Computer Communication Review, volume 39, 2009. Google ScholarDigital Library
- R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu. No power struggles: Coordinated multi-level power management for the data center. In ACM SIGARCH Computer Architecture News, volume 36. ACM, 2008. Google ScholarDigital Library
- B. Sharma, V. Chudnovsky, J. Hellerstein, R. Rifaat, and C. Das. Modeling and synthesizing task placement constraints in google compute clusters. In Proceedings of ACM Symposium on Cloud Computing, 2011. Google ScholarDigital Library
- Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. Cloudscale: Elastic resource scaling for multi-tenant cloud systems. In Proceedings of the ACM Symposium on Cloud Computing, 2011. Google ScholarDigital Library
- A. Verma, P. Ahuja, and A. Neogi. pMapper: power and migration cost aware application placement in virtualized systems. In ACM/IFIP/USENIX Middleware, 2008. Google ScholarDigital Library
- A. Verma, G. Dasgupta, T. Nayak, P. De, and R. Kothari. Server workload analysis for power minimization using consolidation. In Proceedings of the conference on USENIX Annual technical conference. USENIX Association, 2009. Google ScholarDigital Library
- G. von Laszewski, L. Wang, A. Younge, and X. He. Power-aware scheduling of virtual machines in DVFS-enabled clusters. In IEEE International Conference on Cluster Computing and Workshops (CLUSTER), 2009.Google ScholarCross Ref
- X. Wang and M. Chen. Cluster-level feedback power control for performance optimization. In IEEE International Symposium on High Performance Computer Architecture (HPCA), February 2008.Google ScholarCross Ref
- Q. Zhang, J. Hellerstein, and R. Boutaba. Characterizing task usage shapes in Google's compute clusters. In Workshop on Large Scale Distributed Systems and Middleware (LADIS), 2011.Google Scholar
Index Terms
- Dynamic energy-aware capacity provisioning for cloud computing environments
Recommendations
Energy-Aware Profiling for Cloud Computing Environments
Cloud Computing has changed the way in which people use the IT resources today. Now, instead of buying their own IT resources, they can use the services offered by Cloud Computing with reasonable costs based on a "pay-per-use" model. However, with the ...
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing
Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications ...
Optimal resource provisioning for cloud computing environment
The paper presents an efficient cloud resource provisioning approach. The Software as a Service (SaaS) provider leases resources from cloud providers and also leases software as services to SaaS users. The SaaS providers aim at minimizing the payment of ...
Comments