A new ranked Hopfield neural networks approach to QoS parallel scheduling for WDM optical interconnection system

Abstract

In this paper, we propose a novel ranked Hopfield neural-network (RHNN) parallel scheduler for a WDM optical interconnection system (WOPIS), containing a set of Clos-like optical switches and a handful of output FDL-based optical buffers. The RHNN scheduler determines an optimal set of neurons (I/O paths) to be enabled, achieving maximal system throughput and priority differentiation subject to the switch- and buffer-contention-free constraints. The RHNN is specially structured with ranked neurons. With each neuron being associated with an input/output path within WOPIS, the RHNN allows higher-rank neurons (higher-priority and/or lower-delay paths) to disable lower-rank neurons that were enabled during previous iterations. Ranking the neurons unfortunately gives rise to a convergence problem. We present two theorems that supply the sufficient conditions for the RHNN scheduler to converge to the optimal solution. We demonstrate via simulation results that, with the computation time of less than one system time slot, the RHNN scheduler achieves near 100% throughput and multi-level prioritized scheduling.