DL-Assisted Cross-Layer Orchestration in Software-Defined IP-Over-EONs: From Algorithm Design to System Prototype

Abstract

Recently, with the development of IP and elastic optical networks (EONs), the network control and management (NC&M) scheme for IP-over-EONs, which can facilitate effective cross-layer orchestration (XLyr-O), has become an interesting but challenging research topic. In this paper, we consider a software-defined IP-over-EON (SD-IPoEON), leverage deep learning (DL) to analyze and predict the traffic fluctuation on established lightpaths in it, and design a proactive DL-assisted XLyr-O scheme. Specifically, we study the DL-assisted XLyr-O scheme from algorithm design to system prototype. A DL module based on the long/short-term memory based neural network (LSTM-NN) is first designed and optimized for precise IP traffic prediction. Then, we develop algorithms to explore the traffic prediction for realizing proactive XLyr-O to deal with hard/soft failures constantly, i.e., making intelligent online decisions to re-groom and reroute IP flows and to reconfigure lightpaths such that the performance tradeoff among lightpath utilization, congestion probability, and reconfiguration frequency is balanced well. Finally, we implement our proposed algorithm in a small-scale but real SD-IPoEON testbed to prototype the DL-assisted XLyr-O, and conduct experiments with it. Experimental results demonstrate that compared with the reactive benchmark without DL-assistance, our proposal not only invokes less network reconfigurations but also reduces packet losses significantly.