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Stationary analysis of the shortest queue problem

机译:最短排队问题的平稳分析

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A simple analytical solution is proposed for the stationary loss system of two parallel queues with finite capacity K, in which new customers join the shortest queue, or one of the two with equal probability if their lengths are equal. The arrival process is Poisson, service times at each queue have exponential distributions with the same parameter, and both queues have equal capacity. Using standard generating function arguments, a simple expression for the blocking probability is derived, which as far as we know is original. Using coupling arguments and explicit formulas, comparisons with related loss systems are then provided. Bounds are similarly obtained for the average total number of customers, with the stationary distribution explicitly determined on {K,..., 2K}, and elsewhere upper bounded. Furthermore, from the balance equations, all stationary probabilities are obtained as explicit combinations of their values at states (0, k) for 0 ≤ k ≤ K. These expressions extend to the infinite capacity and asymmetric cases, i.e., when the queues have different service rates. For the initial symmetric finite capacity model, the stationary probabilities of states (0, k) can be obtained recursively from the blocking probability. In the other cases, they are implicitly determined through a functional equation that characterizes their generating function. The whole approach shows that the stationary distribution of the infinite capacity symmetric process is the limit of the corresponding finite capacity distributions. Finally, application of the results for limited capacity to mean-field models for large bike-sharing networks with a local JSQ policy is briefly discussed.
机译:针对具有容量K的两个并行队列的固定损失系统,提出了一种简单的分析解决方案,其中新客户加入了最短的队列,或者如果两个客户的长度相等,则两个客户中的一个具有相同的概率。到达过程是泊松,每个队列的服务时间具有相同参数的指数分布,并且两个队列的容量相等。使用标准的生成函数参数,可以得出阻塞概率的简单表达式,据我们所知,它是原始的。然后使用耦合参数和显式公式,提供与相关损失系统的比较。对于客户的平均总数也类似地获得界限,并在{K,...,2K}和其他上限上明确确定固定分布。此外,从平衡方程式中,所有静态概率都是状态(0,k)在0≤k≤K时的值的明确组合。这些表达式扩展到无限容量和非对称情况,即,当队列具有不同服务费。对于初始对称有限容量模型,可以根据阻塞概率递归获得状态(0,k)的平稳概率。在其他情况下,它们是通过表征其生成函数的函数方程式隐式确定的。整个方法表明,无限容量对称过程的平稳分布是相应有限容量分布的极限。最后,简要讨论了将有限容量的结果应用于具有本地JSQ策略的大型自行车共享网络的均值模型的应用。

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