DocumentCode :
2015981
Title :
Optimal Power Management for Server Farm to Support Green Computing
Author :
Niyato, Dusit ; Chaisiri, Sivadon ; Sung, Lee Bu
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore
fYear :
2009
fDate :
18-21 May 2009
Firstpage :
84
Lastpage :
91
Abstract :
Green computing is a new paradigm of designing the computer system which considers not only the processing performance but also the energy efficiency. Power management is one of the approaches in green computing to reduce the power consumption in distributed computing system. In this paper, we first propose an optimal power management (OPM) used by a batch scheduler in a server farm. This OPM observes the state of a server farm and makes the decision to switch the operation mode (i.e., active or sleep) of the server to minimize the power consumption while the performance requirements are met. An optimization problem based on constrained Markov decision process (CMDP) is formulated and solved to obtain an optimal decision of OPM. Given that OPM is used in the server farm, then an assignment of users to the server farms by a job broker is considered. This assignment is to ensure that the cost due to power consumption and network transportation is minimized. The performance of the system is extensively evaluated. The result shows that with OPM the job waiting time can be maintained below the maximum threshold while the power consumption is much smaller than that without OPM.
Keywords :
Markov processes; distributed processing; power aware computing; constrained Markov decision process; distributed computing; green computing; optimal power management; power consumption; server farm; Constraint optimization; Costs; Distributed computing; Energy consumption; Energy efficiency; Energy management; Network servers; Power system management; Processor scheduling; Switches; Green computing; Markov decision process; optimal power management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cluster Computing and the Grid, 2009. CCGRID '09. 9th IEEE/ACM International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3935-5
Electronic_ISBN :
978-0-7695-3622-4
Type :
conf
DOI :
10.1109/CCGRID.2009.89
Filename :
5071858
Link To Document :
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