NapSAC: design and implementation of a power-proportional web cluster

Energy consumption is a major and costly problem in data centers. A large fraction of this energy goes to powering idle machines that are not doing any useful work. We identify two causes of this inefficiency: low server utilization and a lack of power-proportionality. To address this problem we present a design for an power-proportional cluster consisting of a power-aware cluster manager and a set of heterogeneous machines. Our design makes use of currently available energy-efficient hardware, mechanisms for transitioning in and out of low-power sleep states, and dynamic provisioning and scheduling to continually adjust to workload and minimize power consumption. With our design we are able to reduce energy consumption while maintaining acceptable response times for a web service workload based on Wikipedia. With our dynamic provisioning algorithms we demonstrate via simulation a 63% savings in power usage in a typically provisioned datacenter where all machines are left on and awake at all times. Our results show that we are able to achieve close to 90% of the savings a theoretically optimal provisioning scheme would achieve. We have also built a prototype cluster which runs Wikipedia to demonstrate the use of our design in a real environment.

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

[2]  Alan Jay Smith,et al.  Improving dynamic voltage scaling algorithms with PACE , 2001, SIGMETRICS '01.

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

[4]  Scott Shenker,et al.  Scheduling for reduced CPU energy , 1994, OSDI '94.

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

[6]  Guillaume Pierre,et al.  Wikipedia workload analysis for decentralized hosting , 2009, Comput. Networks.

[7]  Wensong Zhang Linux Virtual Server for Scalable Network Services , 2000 .

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

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

[10]  David E. Culler,et al.  Power Optimization - a Reality Check , 2009 .

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

[12]  Randy H. Katz,et al.  An energy case for hybrid datacenters , 2010, OPSR.

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

[14]  Philip Levis,et al.  Policies for dynamic clock scheduling , 2000, OSDI.

[15]  Roy T. Fielding,et al.  Hypertext Transfer Protocol - HTTP/1.1 , 1997, RFC.

[16]  Ricardo Bianchini,et al.  Dynamic cluster reconfiguration for power and performance , 2003 .

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

[18]  Amar Phanishayee,et al.  FAWN: a fast array of wimpy nodes , 2009, SOSP '09.

[19]  Eric A. Brewer,et al.  Lessons from Giant-Scale Services , 2001, IEEE Internet Comput..

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