The Open ROADM ecosystem enables greater flexibility in deploying optical networks through centralized SDN management and intelligent service orchestration. However, since the maintenance signaling is yet to be standardized and implemented within each device, it is difficult to identify the root cause of issues and manage the network effectively when faults propagate in the network. To tackle this problem, this paper presents a proof-of-concept implementation of an alarm correlation and ticketing system for the multi-vendor Open ROADM ecosystem. The proposed system uses a graph-based method to identify the root cause of alarms and generate tickets for network operations teams. The results of our laboratory tests demonstrate the effectiveness of the proposed system in managing alarms in the multi-vendor Open ROADM ecosystem.

This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field.

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error.

The existing dimensioning strategy for translucent, sub-wavelength switching architectures relies on over-provisioning, and consequently, overuse of costly, power-consuming optical-electrical-optical (O/E/O) regenerators. In addition, due to a variety of external phenomena, many physical layer impairments are time-varying, and hence, can strongly degrade network performance. In this work, we introduce a Cross-Layer Optical Network Element (CLONE) used for the dynamic management of physical layer impairments in the network. We investigate the impact of real-time impairment aware routing in a CLONE-enabled optical network with sub-wavelength switching. Simulation results show that the CLONE-enabled network architecture provides improvements in: (1) energy efficiency by optimizing the usage of regenerators, and (2) network performance in terms of the packet loss probability.

In this work we propose anycast routing methods to improve the performance of reconfigurable WDM networks under the variations in the IP traffic. We first investigate anycast communication via impairment-aware anycast routing (IAAR); our simulation results show significant improvement in the blocking probability. We also investigate the proposed load-aware anycast routing (LAAR) for the varying traffic model. From the results we observe that LAAR minimizes the lightpath request loss, by dynamically choosing the anycast configuration based on the network load.

In this paper we discuss the effect of physical impairments on manycasting service over the optical burst- switched (OBS) networks. Signal quality degradation in manycast networks is an important issue and it can occur due to fiber attenuation, splitter switch and amplified spontaneous noise in EDFA. These physical layer impairments causes the signal quality to be weak at the receiver and hence burst may not be detected or lost. Our objective is to select the manycast destinations based on the quality of signal received. We propose a new algorithm,impairmentaware-dynamicmembership(IADM) that takes into account of the physical layer impairments. Based on the simulation results we observe that IADM is more robust and practical, as bursts are scheduled not just on contention but also on the physical layer constraints.

We discuss the effect of physical impairments on manycasting service over optical burst-switched (OBS) networks. Signal quality degradation in manycast networks is an important issue and it can occur due to fiber attenuation, splitter switch, and amplified spontaneous noise in EDFA. These physical layer impairments causes the signal quality to be weak at the receiver and hence burst may not be detected. Our objective is to select the manycast destinations based on resource unavailability and the quality of signal received. We propose three impairment- aware algorithms that take into account of the physical layer impairments. Using extensive simulation results we compute average burst loss probability, both due to contention and signal degradation. These simulation results are verified by the analytical model. We have also compared our results with random destination selection using binomial model and observe that our methods perform better than the random selection method.