Fault Localization in All-Optical Mesh Networks

Fault management is a challenging task in all-optical wavelength division multiplexing (WDM) networks. However, fast fault localization for shared risk link groups (SRLGs) with multiple links is essential for building a fully survival and functional transparent all-optical mesh network. Monitoring trail (m-trail) technology is an effective approach to achieve the goal, whereby a set of m-trails are derived for unambiguous fault localization (UFL). However, an m-trail traverses through a link by utilizing a dedicated wavelength channel (WL), causing a significant amount of resource consumption. In addition, existing m-trail methods incur long and variable alarm dissemination delay. We introduce a novel framework of real-time fault localization in all-optical WDM mesh networks, called the monitoring-burst (m-burst), which aims at initiating a balanced trade-off between consumed monitoring resources and fault localization latency. The m-burst framework has a single monitoring node (MN) and requires one WL in each unidirectional link if the link is traversed by any m-trail. The MN launches short duration optical bursts periodically along each m-trail to probe the links of the m-trail. Bursts along different m-trails are kept non-overlapping through each unidirectional link by scheduling burst launching times from the MN and multiplexing multiple bursts, if any, traversing the link. Thus, the MN can unambiguously localize the failed links by identifying the lost bursts without incurring any alarm dissemination delay. We have proposed several novel m-trail allocation, burst launching time scheduling, and node switch fabric configuration schemes. Numerical results show that the schemes, when deployed in the m-burst framework, are able to localize single-link and multi-link SRLG faults unambiguously, with reasonable fault localization latency, by using at most one WL in each unidirectional link. To reduce the fault localization latency further, we also introduce a novel methodology called nested m-trails. At first, mesh networks are decomposed into cycles and trails. Each cycle (trail) is realized as an independent virtual ring (linear) network using a separate pair of WLs (one WL in each direction) in each undirected link traversed by the cycle (trail). Then, sets of m-trails, i.e., nested m-trails, derived in each virtual network are deployed independently in the m-burst framework for ring (linear) networks. As a result, the fault localization latency is reduced significantly. Moreover, the application of nested m-trails in adaptive probing also reduces the number of sequential probes significantly. Therefore, practical deployment of adaptive probing is now possible. However, the WL consumption of the nested m-trail technique is not limited by one WL per unidirectional link. Thus, further investigation is needed to reduce the WL consumption of the technique.1 yea

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Security of Electrical, Optical and Wireless On-Chip Interconnects: A Survey

The advancement of manufacturing technologies has enabled the integration of more intellectual property (IP) cores on the same system-on-chip (SoC). Scalable and high throughput on-chip communication architecture has become a vital component in today's SoCs. Diverse technologies such as electrical, wireless, optical, and hybrid are available for on-chip communication with different architectures supporting them. Security of the on-chip communication is crucial because exploiting any vulnerability would be a goldmine for an attacker. In this survey, we provide a comprehensive review of threat models, attacks, and countermeasures over diverse on-chip communication technologies as well as sophisticated architectures.Comment: 41 pages, 24 figures, 4 table

Optical Networks and Interconnects

The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity

E2E-OAM in convergent sub-wavelength-MPLS environments

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Fernandez-Palacios, J. Aracil, M. Basham, and M. Georgiades, "E2E-OAM in convergent sub-wavelength-MPLS environments", in Future Network and Mobile Summit, 2012, pp. 1-11This paper presents an End-to-End (E2E) Operations, Administration, and Maintenance (OAM) architecture for Telco networks including a Sub-wavelength domain. It addresses two main issues: compatibility between MPLS networks and different Sub-wavelength technologies, and scalability of the OAM flows across the whole network. The case for OPST Sub-wavelength technology in the data plane has been studied extensively, however this is the first study on a methodology to scale the number of OAM flows in an E2E scenario combing both subwavelength and MPLS switching domains. Finally the inter-carrier issue in E2E OAM is also explored

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions