Neural network for one-shot scheduling problems in multicast packet switching systems

In a multicast packet switching system, multiple output ports can send out packets simultaneously by means of the packet copying function. In realizing such a switching system, the transmission pattern must be determined for successively arising transfer requests, so that the number of transmitted packets is maximized, while satisfying the constraints. This paper considers the one-shot scheduling problem formulated by Chen and colleagues, where all copies of the same packet are sent at the same time, and proposes a new solution method using a neural network. In the proposed solution method using the neural network, the binary neuron, which is suited to the digital techniques and hardware implementation, is employed together with the synchronous update scheme. In order to improve the convergence performance and the accuracy of the solution, an operational equation with an additional heuristic term and a coefficient setting procedure taking account of transfer requests are proposed. By simulations for cases of up to 1000 ports, it is shown that the proposed solution method using the neural network is better in deriving the solution than the conventional method. It is also shown that the considered problem is NP-hard.