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Experimentation for Different Scheduling Policies on Queues: Mixed Differences-in-Q Estimators Based on Little's Law
[Submitted on 28 May 2026 (v1), last revised 14 Jun 2026 (this v · 2026-05-29 · via stat updates on arXiv.org

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Abstract:In data centers, tasks are dispatched to various servers to evenly distribute the workload. When a data center considers implementing a new scheduling algorithm, it typically conducts an A/B test prior to deployment to assess the real-world impact of this new method. However, a straightforward A/B test might be interfered with so-called ``Markovian'' interference. We utilized the Differences-in-Q estimator, as developed by Farias et al. (2022), and introduced mixed Differences-in-Q estimators grounded in Little's Law. We show that our A/B testing methods significantly reduce bias and variance when testing various scheduling policies. Extensive simulations were conducted under scenarios like non-stationary arrival rates, heterogeneous service rates, and communication delays. These simulations highlight the robustness and efficacy of our A/B testing approach.

Submission history

From: Nanshan Jia [view email]
[v1] Thu, 28 May 2026 09:07:44 UTC (720 KB)
[v2] Sun, 14 Jun 2026 00:15:28 UTC (720 KB)