Power Cost Reduction in Distributed Data Centers: A Two-Time-Scale Approach for Delay Tolerant Workloads
Issue No. 01 - Jan. (2014 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.341
Yuan Yao , Univ. of Southern California, Los Angeles, CA, USA
Longbo Huang , Tsinghua Univ., Beijing, China
Abhishek B. Sharma , NEC Labs. America, Inc., Princeton, NJ, USA
Leana Golubchik , Univ. of Southern California, Los Angeles, CA, USA
Michael J. Neely , Univ. of Southern California, Los Angeles, CA, USA
This paper considers a stochastic optimization approach for job scheduling and server management in large-scale, geographically distributed data centers. Randomly arriving jobs are routed to a choice of servers. The number of active servers depends on server activation decisions that are updated at a slow time scale, and the service rates of the servers are controlled by power scaling decisions that are made at a faster time scale. We develop a two-time-scale decision strategy that offers provable power cost and delay guarantees. The performance and robustness of the approach is illustrated through simulations.
Servers, Delay, Optimization, Distributed databases, Algorithm design and analysis, Vectors, Routing
Yuan Yao, Longbo Huang, A. B. Sharma, L. Golubchik and M. J. Neely, "Power Cost Reduction in Distributed Data Centers: A Two-Time-Scale Approach for Delay Tolerant Workloads," in IEEE Transactions on Parallel & Distributed Systems, vol. 25, no. 1, pp. 200-211, 2013.