The rapid rise in energy consumption from large-scale AI workloads in data centers placed the increasing pressures on power grids in recent years. Since grids must maintain real-time bal- ance between supply and demand, there has been heightened interest in job flexibility services from data centers. We propose an adaptive, learning-based contextual restless multi-armed bandit (RMAB) framework in which the grid requests load reductions without requiring visibility into job (re)scheduling decisions. With multiple virtual machine (VM) datasets, the core strategy models a cyclic job queue and batch-level (re)scheduling within each data center as a Markov decision process (MDP), and derives Whittle-index-based policies computed from the learned transition and reward functions via Thompson sampling. To address the challenges of enlarging state space and sparse state visits, we introduce domain-knowledge-enriched refined strategies, including adaptive mixed strategies, gated priors, low-rank smoothing, and offline posterior support. Extensive simulations under both baseline and stress scenarios demonstrate that the cellwise best refined variant exceeds TW in every reported cell of the stress sweep at lower computational cost than EXP4. These results demonstrate the economic value of data center flexibility services and underscore the importance of high-quality, open-source AI workload datasets for developing and evaluating adaptive scheduling algorithms.

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