学习自适应SED以实现异构负载均衡
Learning Adaptive SED for heterogeneous load balancing
AI总结：
针对服务速率未知的异构两服务器负载均衡系统，提出一种在线学习算法，平衡经验SED路由与强制探索，实现有限遗憾并收敛至SED策略。
AI中文摘要：
我们研究了一个具有异构服务速率的两服务器负载均衡系统，这些服务速率对调度器而言是先验未知的。目标是按照最短期望延迟（SED）策略路由客户，但这需要了解服务速率。基于估计的经验策略表现不佳：由于估计误差，经验策略在状态空间的无限区域上与最优策略不一致。我们提出了一种在线学习算法，在学习服务速率的同时收敛到SED。该算法精心平衡了经验SED路由与强制探索阶段，确保对两个服务器进行充分采样。我们证明了我们的算法实现了有限遗憾；这与经典多臂老虎机设置不同，后者遗憾通常随时间对数增长。最后，数值实验展示了我们算法的性能，并强调了强制探索特别有益的场景。
英文摘要：
We study a two-server load balancing system with heterogeneous service rates that are a priori unknown to the dispatcher. The goal is to route customers according to the Shortest--Expected--Delay (SED) policy, but this requires knowledge of the service rates. Empirical policies that route based on estimates perform poorly: due to estimation error, the empirical policy disagrees with the oracle on an infinite region of the state space. We propose an online learning algorithm that converges to SED while learning the service rates. The algorithm carefully balances empirical SED routing with forced exploration phases that guarantee sufficient sampling of both servers. We prove that our algorithm achieves finite regret; this differs from classical Multi-Armed Bandit settings where regret typically grows logarithmically in time. Finally, numerical experiments demonstrate the performance of our algorithm and highlight the regimes in which forced exploration is especially beneficial.
