Power management by load forecasting in web server clusters

The complexity and requirements of web applications are increasing in order to meet more sophisticated business models (web services and cloud computing, for instance). For this reason, characteristics such as performance, scalability and security are addressed in web server cluster design. Due to the rising energy costs and also to environmental concerns, energy consumption in this type of system has become a main issue. This paper shows energy consumption reduction techniques that use a load forecasting method, combined with DVFS (Dynamic Voltage and Frequency Scaling) and dynamic configuration techniques (turning servers on and off), in a soft real-time web server clustered environment. Our system promotes energy consumption reduction while maintaining user’s satisfaction with respect to request deadlines being met. The results obtained show that prediction capabilities increase the QoS (Quality of Service) of the system, while maintaining or improving the energy savings over state-of-the-art power management mechanisms. To validate this predictive policy, a web application running a real workload profile was deployed in an Apache server cluster testbed running Linux.

[1]  Luca Benini,et al.  Compilers and Operating Systems for Low Power , 2012, Springer US.

[2]  Daniel Mossé,et al.  Generalized Tardiness Quantile Metric: Distributed DVS for Soft Real-Time Web Clusters , 2009, 2009 21st Euromicro Conference on Real-Time Systems.

[3]  Daniel Mossé,et al.  Statistical QoS Guarantee and Energy-Efficiency in Web Server Clusters , 2007, 19th Euromicro Conference on Real-Time Systems (ECRTS'07).

[4]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[5]  Java Binding,et al.  GNU Linear Programming Kit , 2011 .

[6]  Venkatesh Pallipadi,et al.  The Ondemand Governor Past, Present, and Future , 2010 .

[7]  Ricardo Bianchini,et al.  Power and energy management for server systems , 2004, Computer.

[8]  Steven Hand,et al.  Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters , 2009, ICAC '09.

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

[10]  Xiaoyun Zhu,et al.  Power-Efficient Response Time Guarantees for Virtualized Enterprise Servers , 2008, 2008 Real-Time Systems Symposium.

[11]  Xue Liu,et al.  Dynamic Voltage Scaling in Multitier Web Servers with End-to-End Delay Control , 2007, IEEE Transactions on Computers.

[12]  Raghunath Othayoth Nambiar,et al.  Transaction Processing Performance Council (TPC): State of the Council 2010 , 2010, TPCTC.

[13]  Daniel Mossé,et al.  Power optimization for dynamic configuration in heterogeneous web server clusters , 2010, J. Syst. Softw..

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

[15]  Mary Shaw,et al.  Leveraging Resource Prediction for Anticipatory Dynamic Configuration , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[16]  David Mosberger,et al.  httperf—a tool for measuring web server performance , 1998, PERV.

[17]  E. N. Elnozahy,et al.  Energy-Efficient Server Clusters , 2002, PACS.

[18]  Hiroto Yasuura,et al.  Voltage scheduling problem for dynamically variable voltage processors , 1998, Proceedings. 1998 International Symposium on Low Power Electronics and Design (IEEE Cat. No.98TH8379).

[19]  Tobias Achterberg,et al.  SCIP: solving constraint integer programs , 2009, Math. Program. Comput..

[20]  Philip S. Yu,et al.  The state of the art in locally distributed Web-server systems , 2002, CSUR.

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

[22]  Klaus Schrape,et al.  The business of forecasting , 2001 .

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

[24]  Claudio Scordino,et al.  Energy-Efficient Real-Time Heterogeneous Server Clusters , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).

[25]  Daniel Mossé,et al.  Load forecasting applied to soft real-time web clusters , 2010, SAC '10.

[26]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[27]  Raphael Guerra,et al.  Attaining soft real-time constraint and energy-efficiency in web servers , 2008, SAC '08.