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.

翻译：在数据中心中，任务被分派到不同服务器以实现负载均衡。当数据中心考虑实施新的调度算法时，通常会在部署前进行A/B测试来评估该新方法在真实场景中的效果。然而，直接的A/B测试可能受到所谓的"马尔可夫"干扰。我们采用了Farias等人（2022）提出的Q差分估计量，并引入了基于利特尔定律的混合Q差分估计量。研究表明，我们的A/B测试方法在测试不同调度策略时显著降低了偏差和方差。我们在非平稳到达率、异构服务率和通信延迟等场景下进行了大量仿真实验，这些实验结果凸显了我们A/B测试方法的鲁棒性和有效性。