Distributed Online and Stochastic Queueing on a Multiple Access Channel

Marcin Bienkowski, T. Jurdzinski, M. Korzeniowski, D. Kowalski
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引用次数: 7

Abstract

We consider the problems of online and stochastic packet queueing in a distributed system of n nodes with queues, where the communication between the nodes is done via a multiple access channel. In the online setting, in each round, an arbitrary number of packets can be injected to nodes’ queues. Two measures of performance are considered: the total number of packets in all queues, called the total load, and the maximum queue size, called the maximum load. We develop a deterministic distributed algorithm that is asymptotically optimal with respect to both complexity measures, in a competitive way. More precisely, the total load of our algorithm is bigger than the total load of any other algorithm, including centralized online solutions, by only an additive term of O(n2), whereas the maximum queue size of our algorithm is at most n times bigger than the maximum queue size of any other algorithm, with an extra additive O(n). The optimality for both measures is justified by proving the corresponding lower bounds, which also separates nearly exponentially distributed solutions from the centralized ones. Next, we show that our algorithm is also stochastically stable for any expected injection rate smaller or equal to 1. This is the first solution to the stochastic queueing problem on a multiple access channel that achieves such stability for the (highest possible) rate equal to 1.
多址信道上的分布式在线和随机排队
研究了一个有n个节点的具有队列的分布式系统中的在线和随机分组排队问题,其中节点之间的通信是通过一个多址通道完成的。在在线设置中,在每一轮中,可以将任意数量的数据包注入节点的队列。考虑两种性能度量:所有队列中的数据包总数(称为总负载)和最大队列大小(称为最大负载)。我们以竞争的方式开发了一种确定性分布式算法,该算法在两种复杂性度量方面都是渐近最优的。更准确地说,我们的算法的总负载比其他任何算法(包括集中在线解决方案)的总负载只增加了一个O(n2)的附加项,而我们的算法的最大队列大小最多是其他任何算法的最大队列大小的n倍,并且额外增加了O(n)。通过证明相应的下界来证明这两种方法的最优性,该下界也将接近指数分布的解与集中的解分开。接下来,我们证明了我们的算法对于任何小于或等于1的预期注入速率也是随机稳定的。这是多址通道随机排队问题的第一个解决方案,它在(最高可能的)速率等于1时实现了这种稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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