论文标题
柔软的实时工作的录取和路由到多群体：索引政策的设计和比较
Admission and routing of soft real-time jobs to multiclusters: Design and comparison of index policies
论文作者
论文摘要
由时间敏感的电子服务应用程序的促进，我们考虑了马尔可夫模型中有效政策的设计，以动态控制一类实时交易的入学和路由到Web服务器的多个异构群集，每个群体都有自己的排队和服务器池。交易带有响应时间截止日期，如果错过了后者，则一直待到完成。每次工作拒绝和截止日期罚款。由于计算最佳策略是棘手的，因此我们旨在设计几乎最佳的启发式策略，这些政策对于大规模系统而言是可行的。制定了四个政策：静态的最佳Bernoulli拆分（BS）策略和三个索引策略，分别基于单独最佳（IO）动作，一步策略改进（PI）和躁动的强盗（RB）索引。一项计算研究表明，PI是此类政策中最好的，始终是最佳的。在纯路线情况下，PI和RB策略几乎都是最佳的。
Motivated by time-sensitive e-service applications, we consider the design of effective policies in a Markovian model for the dynamic control of both admission and routing of a single class of real-time transactions to multiple heterogeneous clusters of web servers, each having its own queue and server pool. Transactions come with response-time deadlines, staying until completion if the latter are missed. Per job rejection and deadline-miss penalties are incurred. Since computing an optimal policy is intractable, we aim to design near optimal heuristic policies that are tractable for large-scale systems. Four policies are developed: the static optimal Bernoulli-splitting (BS) policy, and three index policies, based respectively on individually optimal (IO) actions, one-step policy improvement (PI), and restless bandit (RB) indexation. A computational study demonstrates that PI is the best of such policies, being consistently near optimal. In the pure-routing case, both the PI and RB policies are nearly optimal.
