Simple and effective dynamic provisioning for power-proportional data centers

Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much gain knowing future workload information can bring to dynamic provisioning. In particular, we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the off-line dynamic provisioning problem, which allows us to characterize the optimal solution in a “divide-and-conquer” manner. We then exploit this insight to design two online algorithms with competitive ratios 2 - α and e/ (e - 1 + α), respectively, where 0 ≤ α ≤ 1 is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to α= 1) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to implement. We demonstrate their effectiveness in simulations using real-world traces.

[1]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

[2]  Ming-Yang Kao,et al.  Searching in an unknown environment: an optimal randomized algorithm for the cow-path problem , 1996, SODA '93.

[3]  Isaac Meilijson,et al.  Convex majorization with an application to the length of critical paths , 1979, Journal of Applied Probability.

[4]  Anand Sivasubramaniam,et al.  Managing server energy and operational costs in hosting centers , 2005, SIGMETRICS '05.

[5]  Mi Zhou,et al.  Surge immunity test of personal computer at power lines , 2011, 2011 7th Asia-Pacific International Conference on Lightning.

[6]  Robert Krauthgamer,et al.  Online server allocation in a server farm via benefit task systems , 2001, STOC '01.

[7]  Thomas S. Ferguson,et al.  Who Solved the Secretary Problem , 1989 .

[8]  Lachlan L. H. Andrew,et al.  Greening geographical load balancing , 2011, PERV.

[9]  Kirk Pruhs,et al.  Dynamic speed scaling to manage energy and temperature , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[10]  Xue Liu,et al.  Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment , 2010, 2010 Proceedings IEEE INFOCOM.

[11]  Suman Nath,et al.  Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services , 2008, NSDI.

[12]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[13]  L H AndrewLachlan,et al.  Dynamic right-sizing for power-proportional data centers , 2013 .

[14]  Mor Harchol-Balter,et al.  Optimality analysis of energy-performance trade-off for server farm management , 2010, Perform. Evaluation.

[15]  Albert Y. Zomaya,et al.  A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems , 2010, Adv. Comput..

[16]  Deep Medhi,et al.  Server Operational Cost Optimization for Cloud Computing Service Providers over a Time Horizon , 2011, Hot-ICE.

[17]  Thomas F. Wenisch,et al.  PowerNap: eliminating server idle power , 2009, ASPLOS.

[18]  Lachlan L. H. Andrew,et al.  Power-Aware Speed Scaling in Processor Sharing Systems , 2009, IEEE INFOCOM 2009.

[19]  Daniel Mossé,et al.  Dynamic optimization of power and performance for virtualized server clusters , 2010, SAC '10.

[20]  Patrick Wendell,et al.  DONAR: decentralized server selection for cloud services , 2010, SIGCOMM '10.

[21]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[22]  Prashant J. Shenoy,et al.  Energy-aware load balancing in content delivery networks , 2011, 2012 Proceedings IEEE INFOCOM.

[23]  N. Rasmussen.  Electrical Efficiency Modeling for Data Centers , 2007 .

[24]  Anna R. Karlin,et al.  Competitive randomized algorithms for non-uniform problems , 1990, SODA '90.

[25]  Patrick Jaillet,et al.  Online Routing Problems: Value of Advanced Information as Improved Competitive Ratios , 2006, Transp. Sci..

[26]  J. Koomey.  Worldwide electricity used in data centers , 2008 .

[27]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[28]  Anna R. Karlin,et al.  Competitive snoopy caching , 1986, 27th Annual Symposium on Foundations of Computer Science (sfcs 1986).

[29]  Antony I. T. Rowstron,et al.  Write off-loading: Practical power management for enterprise storage , 2008, TOS.

[30]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[31]  Ripal Nathuji,et al.  Exploiting Platform Heterogeneity for Power Efficient Data Centers , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[32]  Michael I. Jordan,et al.  Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters , 2009, HotCloud.

[33]  Enrique V. Carrera,et al.  Load balancing and unbalancing for power and performance in cluster-based systems , 2001 .

[34]  F. Frances Yao,et al.  A scheduling model for reduced CPU energy , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[35]  Wei Jin,et al.  USENIX Association Proceedings of USITS ’ 03 : 4 th USENIX Symposium on Internet Technologies and Systems , 2003 .

[36]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[37]  F. Mosteller,et al.  Recognizing the Maximum of a Sequence , 1966 .

[38]  Minghua Chen,et al.  Simple and effective dynamic provisioning for power-proportional data centers , 2012, CISS.

[39]  Luiz André Barroso,et al.  The Price of Performance , 2005, ACM Queue.

[40]  Kazuo Iwama,et al.  Average-Case Competitive Analyses for Ski-Rental Problems , 2002, Algorithmica.

[41]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[42]  Bruce M. Maggs,et al.  Cutting the electric bill for internet-scale systems , 2009, SIGCOMM '09.

[43]  Randy H. Katz,et al.  NapSAC: design and implementation of a power-proportional web cluster , 2010, CCRV.

[44]  Ricardo A. Baeza-Yates,et al.  Searching in the Plane , 1993, Inf. Comput..

[45]  Allan Borodin,et al.  On the power of randomization in on-line algorithms , 2005, Algorithmica.

[46]  Lachlan L. H. Andrew,et al.  Online algorithms for geographical load balancing , 2012, 2012 International Green Computing Conference (IGCC).

[47]  Jeffrey S. Chase,et al.  Balance of power: dynamic thermal management for Internet data centers , 2005, IEEE Internet Computing.

[48]  Kirk Pruhs,et al.  Getting the best response for your erg , 2004, TALG.

[49]  Ricardo Bianchini,et al.  Energy conservation in heterogeneous server clusters , 2005, PPoPP.