Energy-efficient job-assignment policy with asymptotically guaranteed performance deviation
J Fu, B Moran
IEEE/ACM Transactions on Networking | IEEE | Published : 2020
We study a job-assignment problem in a large-scale server farm system with geographically deployed servers as abstracted computer components (e.g., storage, network links, and processors) that are potentially diverse. We aim to maximize the energy efficiency of the entire system by effectively controlling carried load on networked servers. A scalable, near-optimal job-assignment policy is proposed. The optimality is gauged as, roughly speaking, energy cost per job. Our key result is an upper bound on the deviation between the proposed policy and the asymptotically optimal energy efficiency, when job sizes are exponentially distributed and blocking probabilities are positive. Relying on Whitt..View full abstract
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Awarded by Australian Research Council
The work of Jing Fu was supported in part by the Australian Research Council (ARC) Centre of Excellence for the Mathematical and Statistical Frontiers (ACEMS) and in part by the ARC Laureate Fellowship FL130100039.