Adaptive Spectrum Control and Management in Elastic Optical Networks

Abstract

Elastic optical networking (EON) has emerged in recent years as a promising solution for implementing flexible bandwidth channels (flexpaths) that efficiently match the allocated bandwidth with the traffic demand using agile granularities of spectrum allocation. However, the additional flexibility in such networks raises challenges in terms of efficient control and management of spectrum resources. Among them, three important issues are (1) mitigation of spectral fragmentation, (2) implementation of impairment awareness and enhancement of robustness against impairments for potentially large-bandwidth flexpaths, and (3) design of an efficient restoration scheme to combat network failures. This paper presents an adaptive spectrum control and management scheme, which includes: dynamic on-demand spectral defragmentation, adaptive combinational quality of transmission (QoT) restoration (ACQR) and supervisory channel-assisted active restoration, to account for the three issues above. We present scalable networking algorithms and experimental demonstrations that address these issues in an EON testbed. Simulation results show that the defragmentation technique is capable of reducing the provisioning blocking probability by half with only one defragmentation module on each link. Then, we also show that the ACQR can efficiently restore many degraded flexpaths on the same impaired link while reducing the restoration blocking probability by a factor of 10 compared with the conventional rerouting method. At last, we show via simulation the advantages of using supervisory channels to determine restoration path quality and selection in EON restorations. This paper also presents experimental demonstrations to corroborate the effectiveness and feasibility of implementing these capabilities in next generation optical networks.