A Restless Bandit Model for Energy-Efficient Job Assignments in Server Farms
Jing Fu, Xinyu Wang, Zengfu Wang, and Moshe Zukerman

TL;DR
This paper develops a scalable, near-optimal job assignment policy for energy-efficient server farms with heterogeneous servers and multiple power modes, proven to be asymptotically optimal and effective in simulations.
Contribution
It introduces a novel, scalable job assignment policy based on the Whittle relaxation technique for large server farms with multiple power modes, achieving asymptotic optimality.
Findings
Policy outperforms baseline methods in simulations
Demonstrates robustness against heavy-tailed job sizes
Proven asymptotic optimality under mild conditions
Abstract
We aim to maximize the energy efficiency, gauged as average energy cost per job, in a large-scale server farm with various storage or/and computing components modeled as parallel abstracted servers. Each server operates in multiple power modes characterized by potentially different service and energy consumption rates. The heterogeneity of servers and multiple power modes complicate the maximization problem, where optimal solutions are generally intractable. Relying on the Whittle relaxation technique, we resort to a near-optimal, scalable job-assignment policy. Under a mild condition related to the service and energy consumption rates of the servers, we prove that our proposed policy approaches optimality as the size of the entire system tends to infinity; that is, it is asymptotically optimal. For the non-asymptotic regime, we show the effectiveness of the proposed policy through…
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Taxonomy
TopicsSmart Grid Energy Management · Green IT and Sustainability · Advanced Bandit Algorithms Research
