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.
|頁（從 - 到）||86-91|
|期刊||IEEE International Conference on High Performance Switching and Routing, HPSR|
|出版狀態||Published - 9 十二月 2013|
|事件||2013 IEEE 14th International Conference on High Performance Switching and Routing, HPSR 2013 - Taipei, Taiwan|
持續時間: 8 七月 2013 → 11 七月 2013