Network traffic modelling and router performance optimization using fuzzy logic and genetic algorithms
- Publication Type:
- Thesis
- Issue Date:
- 2007
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Accurate computer network traffic models are required for many network tasks
such as network analysis, performance optimization and areas of traffic engineering
such as avoiding congestion or guaranteeing a specific quality of service (QoS)
to an application. Existing traffic modelling techniques rely on precise mathematical
analysis of extensive measured data such as packet arrival time, packet
size and server-side or client-side round trip time. With the advent of high speed
broadband networks, gathering an acceptable quantity of data needed for the
precise representation of traffic is a difficult, time consuming, expensive and in
some cases almost an impossible task. A possible alternative is to employ fuzzy
logic based models which can represent processes characterized by imprecise data,
which is generally easier to gather. The effectiveness of these models has been
demonstrated in many industrial applications. This work develops fuzzy logic
based traffic models using imprecise data sets that can be obtained realistically.
Optimizing the performance of a router requires the optimization of a number of
conflicting objectives. A possible approach is to express it as a multi-objective
problem. Multi-objective evolutionary algorithms (MOEA) can be used for solving
such problems. This research proposes two fuzzy logic based traffic models:
fuzzy group model and fuzzy state model. These models together with MOEA are
used to propose a simple and fast router buffer management scheme. The developed
fuzzy group model includes a parameter which is also useful for measuring
the irregular traffic patterns known as burstiness. The experimental results are
promising.
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