Bandwidth-demand prediction in virtual path in ATM networks using genetic algorithms
Introduction
The Virtual Path (VP) concept is considered an effective means to manage an ATM network [1], [2]. The benefits of using VPs are many: reduced node processing per virtual circuit, reduced call setup time, simplified node structure, and proper control over routing and bandwidth management. VP bandwidth control improves transmission efficiency for a given call blocking probability (and vice versa) [1]. Improvement in efficiency is achieved through the multiplexing of the physical link bandwidth among the VPs that share the same physical link. The VP bandwidth is dynamically altered in accordance to the traffic demand. There are many schemes suggested in the literature for VP bandwidth control, some centralized [3], [4], [5], [6], [7], [8], [9], [10] and others distributed [11], [12], [13]. For efficient management of the VP bandwidth, an accurate estimate of the bandwidth-demand of the traffic flowing through the VPs is required. In order to make an accurate estimate of the bandwidth-demand, a proper understanding of the behavior of the traffic is required.
The behavior of the traffic is usually expressed in terms of its past statistical properties. For short term (less than one hour) estimate of traffic behavior the statistical properties are assumed to vary slowly and hence daily, weekly and seasonal cycles are not considered, instead the peak demand during the previous observation period is considered as the current demand [13]. Characterizing the traffic by on-line measurement of the large deviation rate function (entropy) is studied in [14], [15]. In this paper we characterize the traffic using bandwidth-demand patterns within a VP. The bandwidth-demand patterns have to be learned to predict the future behavior of the traffic.
Learning algorithms have been suggested for adaptive routing in ISDN [16] and multicast routing [17] based on the network load. We propose an evolutionary-genetic approach to learn the dynamic behavior of traffic on the basis of bandwidth-demand patterns in VPs. We also describe as to how to use the bandwidth-demand patterns, in order to make short term bandwidth-demand predictions in VPs that is subsequently used for VP bandwidth management. From our studies, it is observed that the genetic approach naturally captures the short-term trend and variation in bandwidth-demand for both Poisson and Self-Similar call arrivals.
The remaining part of the paper is divided as follows. Section 2 describes the proposed approach by developing the framework for using genetic algorithms in such class of problems. Simulation studies and the results are discussed in Section 3. Section 4 concludes the paper by exploring the scope for further research.
Section snippets
The evolutionary-genetic algorithm
A genetic algorithm is a heuristic approach that applies the natural genetic ideas of natural selection, mutation and survival of the fittest. A rigorous mathematical formalism was introduced by Holland and his collaborators, and is till date the basis for genetic algorithms. A comprehensive review of this field can be found in the book by Goldberg [18].
The algorithm uses a set of offered solutions called a “population”. Each solution called an “individual”, can be any solution in the solution
Simulation experiments
In this section we verify the effectiveness of the proposed bandwidth-demand predictor through simulation. First we describe the simulation environment and then explain the various experiments conducted and the inferences derived.
Conclusion
Virtual Path (VP) bandwidth control improves transmission efficiency in an ATM network. An accurate estimate of the bandwidth-demand within a VP leads to efficient VP bandwidth control. So far the statistical methods were employed to predict the bandwidth-demand. In this paper we have presented an Evolutionary-Genetic Approach (EGA) to predict bandwidth-demand patterns within a VP. We have quantified the efficiency of this scheme in terms of the Degree of Learning. Factors affecting the
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