Modeling a multi-queue network node with a fuzzy predictor
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. (C) 2008 Elsevier B.V. All rights reserved.
nonlinear programming simulation fuzzy system models network capacity planning