The BAR Approach for Multiclass Queueing Networks with SBP Service Policies

Q1 Mathematics Stochastic Systems Pub Date : 2024-05-02 DOI:10.1287/stsy.2023.0011
Anton Braverman, J. G. Dai, Masakiyo Miyazawa
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Abstract

The basic adjoint relationship (BAR) approach is an analysis technique based on the stationary equation of a Markov process. This approach was introduced to study heavy-traffic, steady-state convergence of generalized Jackson networks in which each service station has a single job class. We extend it to multiclass queueing networks operating under static-buffer-priority (SBP) service disciplines. Our extension makes a connection with Palm distributions that allows one to attack a difficulty arising from queue-length truncation, which appears to be unavoidable in the multiclass setting. For multiclass queueing networks operating under SBP service disciplines, our BAR approach provides an alternative to the “interchange of limits” approach that has dominated the literature in the last twenty years. The BAR approach can produce sharp results and allows one to establish steady-state convergence under three additional conditions: stability, state space collapse (SSC) and a certain matrix being “tight.” These three conditions do not appear to depend on the interarrival and service-time distributions beyond their means, and their verification can be studied as three separate modules. In particular, they can be studied in a simpler, continuous-time Markov chain setting when all distributions are exponential. As an example, these three conditions are shown to hold in reentrant lines operating under last-buffer-first-serve discipline. In a two-station, five-class reentrant line, under the heavy-traffic condition, the tight-matrix condition implies both the stability condition and the SSC condition. Whether such a relationship holds generally is an open problem.
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采用 SBP 服务策略的多类排队网络的 BAR 方法
基本邻接关系(BAR)方法是一种基于马尔可夫过程静态方程的分析技术。引入这种方法是为了研究广义杰克逊网络的大流量稳态收敛问题,在这种网络中,每个服务站都有一个作业类别。我们将其扩展到在静态缓冲优先(SBP)服务规则下运行的多级排队网络。我们的扩展将 Palm 分布联系起来,从而解决了队列长度截断所带来的难题,这在多类别设置中似乎是不可避免的。对于在 SBP 服务规则下运行的多类队列网络,我们的 BAR 方法提供了一种替代方法,可用于替代过去二十年中占主导地位的 "极限互换 "方法。BAR 方法可以产生尖锐的结果,并允许人们在三个附加条件下建立稳态收敛:稳定性、状态空间坍缩(SSC)和特定矩阵 "紧密"。这三个条件似乎并不超出到达间隔和服务时间分布的范围,其验证可作为三个独立模块进行研究。特别是,当所有分布都是指数分布时,可以在更简单的连续时间马尔可夫链环境中研究它们。举例来说,这三个条件在按照 "最后缓冲-优先服务 "规则运行的可重入线路中都是成立的。在一条两站五班的重入线路中,在大流量条件下,紧矩阵条件意味着稳定性条件和 SSC 条件。这种关系是否普遍成立还是一个未决问题。
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来源期刊
Stochastic Systems
Stochastic Systems Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
3.70
自引率
0.00%
发文量
18
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