This paper introduces a novel and simplified cost model for designing and evaluating a Central Office Rearchitected as a Datacenter (CORD). The model includes equipment and elements for the realization of optical, packet switching, and data center parts with a special focus not only on relative costs but also on power consumption figures. The cost model is then applied to the design and comparison of a metropolitan area network (MAN) including both aggregation and metrocore nodes following several MAN node architectures based on CORD-like leaf-and-spine fabric. In particular, equipment disaggregation at the Central Offices, both on the packet-switching and optical components, can provide important cost savings to telco operators. On the other hand, incorporating computing/storage capabilities in the MAN for the realization of multiaccess edge computing (MEC) has a significant impact on the total network cost but especially on power consumption.

Comprehensive model for technoeconomic studies of next-generation central offices for metro networks

As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of the performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing what we believe to be a novel functional block called the security operation center, describing its architecture, specifying key requirements on the supported functionalities, and providing guidelines on its integration with optical-layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between the ML accuracy and run-time complexity.

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Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This article focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL), and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.

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Machine Learning for Optical Network Security Monitoring: A Practical Perspective

We present an open design of a filterless add/drop reconfigurable optical add/drop multiplexer module with a NETCONF northbound interface. Compared to commercial offerings, the device design is openly documented and available for detailed inspection. Performance is evaluated with up to seven adjacent high-speed 100 Gbps signals on a 50 GHz frequency grid.

Opening up ROADMs: a filterless add/drop module for coherent-detection signals

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semidisaggregated metro nodes with compute and storage capabilities, which interface effectively with both 5G access and multi-Tbit/s elastic optical networks in the core. In this paper, we report the automated deployment of 5G services, in particular, a public safety video surveillance use case employing low-latency object detection and tracking using on-camera and on-the-edge analytics. The demonstration features flexible deployment of network slice instances, implemented in terms of European Telecommunications Standards Institute (ETSI) network function virtualization network services. We summarize the key findings in a detailed analysis of end-to-end quality of service, service setup time, and soft-failure detection time. The results show that the round-trip time over an 80 km link is under 800 µs and the service deployment time is under 180 s.

Demonstration of latency-aware 5G network slicing on optical metro networks

Operators' network management continuously measures network health by collecting data from the deployed network devices; data is used mainly for performance reporting and diagnosing network problems after failures, as well as by human capacity planners to predict future traffic growth. Typically, these network management tools are generally reactive and require significant human effort and skills to operate effectively. As optical networks evolve to fulfil highly flexible connectivity and dynamicity requirements, and supporting ultra-low latency services, they must also provide reliable connectivity and increased network resource efficiency. Therefore, reactive human-based network measurement and management will be a limiting factor in the size and scale of these new networks. Future optical networks must support fully automated management, providing dynamic resource re-optimization to rapidly adapt network resources based on predicted conditions and events; identify service degradation conditions that will eventually impact connectivity and highlight critical devices and links for further inspection; and augment rapid protection schemes if a failure is predicted or detected, and facilitate resource optimization after restoration events. Applying automation techniques to network management requires both the collection of data from a variety of sources at various time frequencies, but it must also support the capability to extract knowledge and derive insight for performance monitoring, troubleshooting, and maintain network service continuity. Innovative analytics algorithms must be developed to derive meaningful input to the entities that orchestrate and control network resources; these control elements must also be capable of proactively programming the underlying optical infrastructure. In this article, we review the emerging requirements for optical network management automation, the capabilities of current optical systems, and the development and standardization status of data models and protocols to facilitate automated network monitoring. Finally, we propose an architecture to provide Monitoring and Data Analytics (MDA) capabilities, we present illustrative control loops for advanced network monitoring use cases, and the findings that validate the usefulness of MDA to provide automated optical network management.

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O. Gonzlez

Papers

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases