Parallel QoS scheduling for WDM optical interconnection system using a new ranked hopfield neural network

In this paper, we propose a parallel QoS scheduler for a WDM optical interconnection system (WOPIS), using a new ranked Hopfield neural-network (RHNN). The WOPIS contains 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. Structured with ranked neurons, the RHNN allows higher-rank neurons (higher-priority and/or lower-delay paths) to disable lower-rank neurons that have been enabled during previous iterations. Ranking the neurons unfortunately gives rise to a convergence problem. We present two theorems that give the sufficient conditions for the RHNN scheduler to converge to the optimal solution. We demonstrate via simulation results that, with the computation time within one system slot time, the RHNN scheduler achieves near 100% throughput and multi-level prioritized scheduling.