TL;DR
This paper introduces a robust RMAB framework for data center VM scheduling to enhance grid demand response, utilizing a mixed strategy with Whittle-index policies and UCB to improve learning efficiency and robustness.
Contribution
It develops a novel mixed-strategy RMAB approach combining Whittle-index policies with UCB to address uncertainties in VM rescheduling for demand response.
Findings
The mixed-strategy algorithm outperforms pure Thompson-Whittle in noisy conditions.
The approach remains robust across different state-space sizes.
It surpasses the performance of the EXP4 framework.
Abstract
Energy demands from data centers have surged and stressed the grid in recent years. Electric grids require balancing supply and demand every second, motivating demand response (reduction) from large loads, including data centers. This can be achieved by rescheduling jobs on physical machines. Its real-time implementation is uncertain due to fluctuating resource utilization, and rescheduling incurs quality-of-service (QoS) losses that providers are unwilling to disclose. We propose a restless multi-arm bandit (RMAB) framework in which the grid operator requests load reductions without access to detailed job-rescheduling procedures. Using the open-source virtual machine (VM) datasets, we model job arrivals and rescheduling at each data center as a restless arm in a Markov decision process (MDP), and derive Whittle-index-based policies based on the learned transition function via Thompson…
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