论文标题
通过索引政策达到最小损失工作路由，以达到平行的异质多层队列
Towards minimum loss job routing to parallel heterogeneous multiserver queues via index policies
论文作者
论文摘要
本文考虑了马尔可夫型模型，用于均质流量到并行异质队列的最佳动态路由，每个流量都有其自己的有限输入缓冲区和服务器池，在此，缓冲区和服务器池尺寸以及服务率可能会有所不同。主要目标是确定具有较低复杂性的基于启发式指数的路由策略，该策略始终达到平均损失率几乎最低（或等效地，最大吞吐量率）。第二个目标是在计算需求和经验绩效方面比较替代政策。可以有效地计算出的新型路由策略是基于惠特尔（Whittle）不安强盗（RB）索引的二阶扩展而开发的，因为该模型后者是恒定的。还为通过策略改进（PI）获得的计算要求更高的索引策略提供了新的结果，包括它减少到对称缓冲区和服务器池大小下的最短队列路由。一项数值研究表明，在考虑的实例中，提出的RB指数策略几乎是最佳的，并且显着优于先前提出的索引策略。
This paper considers a Markovian model for the optimal dynamic routing of homogeneous traffic to parallel heterogeneous queues, each having its own finite input buffer and server pool, where buffer and server-pool sizes, as well as service rates, may differ across queues. The main goal is to identify a heuristic index-based routing policy with low complexity that consistently attains a nearly minimum average loss rate (or, equivalently, maximum throughput rate). A second goal is to compare alternative policies, with respect to computational demands and empirical performance. A novel routing policy that can be efficiently computed is developed based on a second-order extension to Whittle's restless bandit (RB) index, since the latter is constant for this model. New results are also given for the more computationally demanding index policy obtained via policy improvement (PI), including that it reduces to shortest queue routing under symmetric buffer and server-pool sizes. A numerical study shows that the proposed RB index policy is nearly optimal across the instances considered, and substantially outperforms several previously proposed index policies.
