Parallel prioritized scheduling for WDM optical switching system

Abstract

Packet scheduling for WDM optical switching systems requires exceedingly low latency processing, making it impractical to be realized by non-parallel based algorithms. In this paper, we propose a new recurrent discrete-time synchronous ranked neural-network (DSRN) for parallel prioritized scheduling. The DSRN is structured with ranked neurons and is capable of operating in a fully parallel (i.e., synchronous) discrete-time manner, and thus can be implemented in digital systems. We then design a DSRN scheduler for a previously proposed experimental WDM optical switching system (WOPIS). For newly arriving packets, the DSRN scheduler determines in real time an optimal set of input/output paths within WOPIS, achieving maximal throughput and priority differentiation subject to the switch- and buffer-contention-free constraints. We delineate via a theorem that DSRN will converge to the optimal solution. The theorem also provides a theoretical upper bound of the convergence latency, O(H), where H is the switch port count. Finally, we demonstrate that, via CUDA-based simulations, the DSRN scheduler achieves near-optimal throughput and prioritized scheduling, with nearly O(logH) convergence latency.