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Modeling a multi-queue network node with a fuzzy predictor

机译：用模糊预测器对多队列网络节点建模

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摘要

Capacity planning of IP-based networks is a difficult task. Ideally, in order to estimate the maximum amount of traffic that can be carried by the network, without violating QoS requirements such as end-to-end delay and packet loss, it is necessary to determine the queue length distribution of the network nodes under different traffic conditions. When per-flow guarantees are required (e.g., VoIP traffic), it is also necessary to determine the impact of the queue behavior on the performance of individual flows. Analytical models for queue length distribution are available only for relatively simple traffic patterns. This paper proposes a generic method for building a fuzzy predictor for modeling the behavior of a DiffServ node with multiple queues. The method combines nonlinear programming (NLP) and simulation to build a fuzzy predictor capable of determining the performance of a DiffServ node subjected to both per-flow and aggregated performance guarantees. This approach does not require deriving an analytical model, and can be applied to any type of traffic. In this paper, we employ the fuzzy approach to model the behavior of a multi-queue node where (aggregated ON-OFF) VoIP traffic and (self-similar) data traffic compete for the network resources.
机译：基于IP的网络的容量规划是一项艰巨的任务。理想情况下，为了在不违反端到端延迟和数据包丢失等QoS要求的前提下，估算网络可以承载的最大流量，有必要确定不同网络节点的队列长度分布交通状况。当需要按流保证时（例如VoIP流量），还需要确定队列行为对单个流性能的影响。队列长度分布的分析模型仅适用于相对简单的流量模式。本文提出了一种用于构建模糊预测器的通用方法，以对具有多个队列的DiffServ节点的行为进行建模。该方法结合了非线性规划（NLP）和仿真，以构建能够确定DiffServ节点的性能的模糊预测器，该节点同时受到按流和聚合性能保证的影响。这种方法不需要导出分析模型，并且可以应用于任何类型的流量。在本文中，我们采用模糊方法对多队列节点的行为进行建模，其中（聚合的ON-OFF）VoIP流量和（自相似）数据流量争夺网络资源。

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