Abstract
The Hopfield neural network is suggested for dynamic multicast routing in communication network of arbitrary topology and under variable traffic conditions. A new algorithm takes into account not only the most important parameters describing the actual network state (the network topology, link and router bandwidths, estimated link delays, and the traffic density), but also the history of link/router occupancy. The goal of the paper is to find the Pareto optimal path for multicast routing case and to avoid possible packets loss, due to heavy traffic. The effectiveness of the new routing algorithm has been verified under various network topologies and traffic conditions.DOI: http://dx.doi.org/10.5755/j01.eee.19.3.3703
Highlights
Standard communication in computer networking comprises the transmission of data from one source (S) to single destination (D), which is known as the unicast transmission
The Hopfield neural network is suggested for dynamic multicast routing in communication network of arbitrary topology and under variable traffic conditions
A new algorithm takes into account the most important parameters describing the actual network state, and the history of link/router occupancy
Summary
Standard communication in computer networking comprises the transmission of data from one source (S) to single destination (D), which is known as the unicast (i.e., point-to-point or S-D pair) transmission. The problem of finding the shortest path from a single source to a single destination has some well-known polynomial algorithmic solutions, such as Dijkstra’s [6] and Bellman-Ford’s [6], but the problem is computationally very hard, in large-scale networks and constrained problems In their well-known paper Hopfield and Tank [10] introduced an analog neural network (known as the HNN) suitable for solving different constrained and computationally hard optimization problems – among others the well-known TSP problem. Note that in real communication networks, due to particular limitations dictated by technological and/or commercial requirements, some parameters affecting the routing path are fixed In this case, instead of standard optimization method more appropriate is to use Pareto optimization [11]. The new algorithm uses Hopfield-like neural network for optimizing, in Pareto sense, the cost of the total Steiner tree [6], and minimizing the total cost whit regard to different network parameters
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