具有复用增益的网络性能保证的流量工作负载包络

Massieh Kordi Boroujeny, B. L. Mark, Y. Ephraim
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引用次数: 1

摘要

随机网络演算涉及使用流量绑定或信封来进行准入控制和资源分配决策,以提供端到端的服务质量保证。为了在实践中应用网络演算,流量包络应该:(i)可以很容易地确定任意流量源,(ii)可以通过交通法规强制执行,(iii)产生统计复用增益。现有的流量包络通常最多满足其中两个属性。基于到达过程矩生成函数(MGF)的众所周知的流量包络只满足第三个性质。我们提出了一种新的流量包络,该流量包络是基于将流量提供给恒定服务速率队列而得到的工作负载进程的MGF。我们展示了这个流量工作负载信封可以实现所有三个属性,并导致一个提供随机延迟保证的网络服务框架。我们用两种突发交通模型:马尔可夫开关流体模型和马尔可夫调制泊松过程模型(MMPP)证明了交通负载包络的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic Workload Envelope for Network Performance Guarantees with Multiplexing Gain
Stochastic network calculus involves the use of a traffic bound or envelope to make admission control and resource allocation decisions for providing end-to-end quality-of-service guarantees. To apply network calculus in practice, the traffic envelope should: (i) be readily determined for an arbitrary traffic source, (ii) be enforceable by traffic regulation, and (iii) yield statistical multiplexing gain. Existing traffic envelopes typically satisfy at most two of these properties. A well-known traffic envelope based on the moment generating function (MGF) of the arrival process satisfies only the third property. We propose a new traffic envelope based on the MGF of the workload process obtained from offering the traffic to a constant service rate queue. We show that this traffic workload envelope can achieve all three properties and leads to a framework for a network service that provides stochastic delay guarantees. We demonstrate the performance of the traffic workload envelope with two bursty traffic models: Markov on-off fluid and Markov modulated Poisson Process (MMPP).
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