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Stable Scheduling Policies for Maximizing Throughput in Generalized Constrained Queueing Systems

机译:最大化约束排队系统中吞吐量最大化的稳定调度策略

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We consider a class of queueing networks referred to as "generalized constrained queueing networks" which form the basis of several different communication networks and information systems. These networks consist of a collection of queues such that only certain sets of queues can be concurrently served. Whenever a queue is served, the system receives a certain reward. Different rewards are obtained for serving different queues, and furthermore, the reward obtained for serving a queue depends on the set of concurrently served queues. We demonstrate that the dependence of the rewards on the schedules alter fundamental relations between performance metrics like throughput and stability. Specifically, maximizing the throughput is no longer equivalent to maximizing the stability region; we therefore need to maximize one subject to certain constraints on the other. Since stability is critical for bounding packet delays and buffer overflow, we focus on maximizing the throughput subject to stabilizing the system. We design provably optimal scheduling strategies that attain this goal by scheduling the queues for service based on the queue lengths and the rewards provided by different selections. The proposed scheduling strategies are however computationally complex. We subsequently develop techniques to reduce the complexity and yet attain the same throughput and stability region. We demonstrate that our framework is general enough to accommodate random rewards and random scheduling constraints.
机译:我们考虑一类称为“广义约束排队网络”的排队网络,它构成了几种不同的通信网络和信息系统的基础。这些网络由一组队列组成,因此只能同时为某些队列集提供服务。每当提供队列时,系统都会获得一定的奖励。为服务不同的队列获得不同的奖励,此外,为服务队列获得的奖励取决于同时服务的队列的集合。我们证明了奖励对时间表的依赖性改变了性能指标(如吞吐量和稳定性)之间的基本关系。具体而言,最大化吞吐量不再等同于最大化稳定区域;而是因此,我们需要最大化一个受另一个约束的约束。由于稳定性对于限制数据包延迟和缓冲区溢出至关重要,因此我们将重点放在最大化吞吐量上,以稳定系统。我们设计了可证明的最佳调度策略,该策略通过基于队列长度和不同选择所提供的奖励来调度服务队列来实现此目标。然而,所提出的调度策略在计算上是复杂的。随后,我们开发了降低复杂性并获得相同吞吐量和稳定性区域的技术。我们证明我们的框架足够通用,可以容纳随机奖励和随机调度约束。

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