The combination of space division multiplexing technology with elastic optical networks allows to overcome the possible capacity crunch in backbone networks and also improves network flexibility by jointly managing spectral and spatial resources. However, against this background implemented by multi-core fibers, the interaction between spatial modes will appear as signal crosstalk, thereby affecting the service’s transmission quality. Spectrum resources without crosstalk are always preferred for the services to guarantee quality of service, possibly resulting in the spectrum fragmentation. Conversely, if resources with crosstalk are selected for services to reduce fragments, it may lead to serious crosstalk on the services already carried in the adjacent cores. To achieve a tradeoff between these two factors, this paper firstly exploits the association rule mining method to quantitatively analyze the potential correlation between them. By executing FP-growth mining algorithm, rules not beneficial to service provisioning will be filtered out. Then, an association rules-based core and spectrum assignment algorithm is presented, considering transmission requirements for different levels of services. Simulation results indicate the presented strategy can decrease the proportion of services affected by crosstalk and also reduce the possibility of fragments generation. Additionally, it can effectively make improvement on the blocking and resource utilization.

Core and Spectrum Allocation Based on Association Rules Mining in Spectrally and Spatially Elastic Optical Networks

pdf/a-review-of-machine-learning-based-failure-management-in-1y52ixo7.pdf

A review of machine learning-based failure management in optical networks

Optical performance monitoring (OPM) and the corresponding telemetry systems play an important role in modern optical transport networks based on software-defined networking (SDN). There have been extensive studies and standardization activities to build high-speed and high-accuracy OPM/telemetry systems that can ensure sufficient monitoring data for effective network control and management. However, current solutions for OPM/telemetry assume that control and management planes (C/M-plane) always provide sufficient bandwidth (BW) to deliver telemetry data. Unfortunately, in the event of several concurrent network failures (e.g., following a large-scale disaster), C/M-plane networks can become heavily degraded and/or unstable, and even experience isolation of some of their parts. Under such circumstances, the existing OPM systems would hardly function. To enhance resiliency and to ensure the quick recovery of OPM/telemetry in case of disaster, we propose an approach for quick recreation of OPM and for achieving robust telemetry based on OpenConfig YANG. Our proposal addresses three key problems: (1) how to quickly recreate the lost OPM capability, (2) how to address the mismatch between the high data rate of OPM and the low BW in the C/M-plane network, and (3) how to flexibly reconfigure the telemetry system to be adaptive to sudden BW changes in the C/M-plane network. We implement a testbed and experimentally demonstrate that our proposal can tolerate low post-disaster bandwidth and can adapt the telemetry system following the changing conditions of the C/M-plane network.

Emergency OPM Recreation and Telemetry for Disaster Recovery in Optical Networks

This paper proposes a scheme of alarm analysis and failure prediction in optical transport network (OTN), by using machine learning, deep learning, and data from the network operating record.

Machine Learning Based Alarm Analysis and Failure Forecast in Optical Networks

Fault location is an essential part of optical network operation and maintenance, and network operators have expectations to achieve highly accurate and precise fault location for reducing the cost of fault recovery. However, due to the scale of such networks, the volume of monitoring data (MD) is quite large, which poses a great challenge for fault location. An attention mechanism is an effective way to focus on the important information from massive input for the current task, which originates from the study of human vision. Targeting component fault location in optical networks, we propose an attention mechanism-based strategy, which consists of a sequence attention mechanism (SAT), a channel attention mechanism (CAT), a graph attention mechanism (GAT), and a fully connected neural network (FCNN). SAT, CAT, and GAT are applied for link, node, and network representation, respectively, taking corresponding MD as input. The FCNN is responsible for analyzing the correlation between MD and completing the fault location decision. All three attention mechanisms can filter out the more critical MD, assisting the FCNN to make more accurate decisions. We compare the performance of the proposed strategy and artificial neural networks (ANNs) in partial telemetry scenarios. Simulation results indicate that our strategy outperforms ANNs with respect to the accuracy of fault location by focusing on more critical MD and achieves a maximum improvement by 5.6%. Moreover, its feasibility with real data is verified on an experimental testbed consisting of hybrid optical-electrical switching nodes. Extensive results show that our strategy has the potential to achieve highly accurate fault location in real networks. 

Multiple attention mechanisms-driven component fault location in optical networks with network-wide monitoring data

A method based on machine learning and association rules mining was proposed. Experiments show that the approach is practical in alarm compression, and the performance of KNA method is better than the classical Apriori algorithm.

Alarm Compression Based on Machine Learning and Association Rules Mining in Optical Networks

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Alarm Compression Based on Machine Learning and Association Rules Mining in Optical Networks