论文标题
通过模型数据独立控制稳定排队网络
Stabilizing Queuing Networks with Model Data-Independent Control
论文作者
论文摘要
经典排队网络控制策略通常依赖于模型数据的准确知识，即到达和服务率。但是，此类数据并非总是可用的，并且可能是时间变化的。为了应对这一挑战，我们考虑了仅依靠交通状态观察和网络拓扑的一类模型独立的（MDI）控制策略。具体而言，我们专注于可以在集中式和分散策略下稳定多级马尔可夫排队网络的MDI控制策略。控制措施包括路由，测序和持有。通过扩展路线并构建分段线性测试功能，我们得出了一个易于使用的标准，可以检查给定的MDI策略下多级网络的稳定性。对于可稳定的多级网络，我们表明存在着集中的，稳定的MDI政策。对于可稳定的单层网络，我们进一步表明，存在分散的，稳定的MDI政策。此外，对于这两种设置，我们构建了明确的策略，这些策略可达到最大吞吐量并呈现数值示例以说明结果。
Classical queuing network control strategies typically rely on accurate knowledge of model data, i.e., arrival and service rates. However, such data are not always available and may be time-variant. To address this challenge, we consider a class of model data-independent (MDI) control policies that only rely on traffic state observation and network topology. Specifically, we focus on the MDI control policies that can stabilize multi-class Markovian queuing networks under centralized and decentralized policies. Control actions include routing, sequencing, and holding. By expanding the routes and constructing piecewise-linear test functions, we derive an easy-to-use criterion to check the stability of a multi-class network under a given MDI policy. For stabilizable multi-class networks, we show that a centralized, stabilizing MDI policy exists. For stabilizable single-class networks, we further show that a decentralized, stabilizing MDI policy exists. In addition, for both settings, we construct explicit policies that attain maximal throughput and present numerical examples to illustrate the results.
