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

An hybrid prediction-based routing approach for reducing routing inaccuracy in optical transport networks

© 2014 IEEE. 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.The advent of network technologies such as Automatically Switched Optical Networks (ASON) and Generalized Multiprotocol Label Switching (GMPLS) pave the way to the deployment of flexible optical transport networks (OTNs). The flexibility of OTNs is a feature highly demanded in dynamic scenarios where lightpaths are continuously set up and torn down on a short-term basis. Unfortunately, the availability and accuracy of network state information in dynamic scenarios are both limited, causing a severe impact on both performance and scalability of Routing and Wavelength Assignment (RWA) algorithms. In this paper we devise a promising routing scheme so-called Hybrid Prediction-based Routing (HPBR). HPBR combines prediction strategies with a novel method to select the most suitable routing metric, aiming at reducing both the dissemination of network state information and the blocking probability. Our findings validate that the proposed scheme significantly reduces the blocking probability compared with other routing schemes, while avoiding the need to periodically disseminate network state information.This work was supported by the Spanish Ministry of Economy under contract TEC2012-34682, and the Catalan Research Council (CIRIT) under contract 2009 SGR1508.Peer ReviewedPostprint (author's final draft

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

A Survey on the Path Computation Element (PCE) Architecture

Quality of Service-enabled applications and services rely on Traffic Engineering-based (TE) Label Switched Paths (LSP) established in core networks and controlled by the GMPLS control plane. Path computation process is crucial to achieve the desired TE objective. Its actual effectiveness depends on a number of factors. Mechanisms utilized to update topology and TE information, as well as the latency between path computation and resource reservation, which is typically distributed, may affect path computation efficiency. Moreover, TE visibility is limited in many network scenarios, such as multi-layer, multi-domain and multi-carrier networks, and it may negatively impact resource utilization. The Internet Engineering Task Force (IETF) has promoted the Path Computation Element (PCE) architecture, proposing a dedicated network entity devoted to path computation process. The PCE represents a flexible instrument to overcome visibility and distributed provisioning inefficiencies. Communications between path computation clients (PCC) and PCEs, realized through the PCE Protocol (PCEP), also enable inter-PCE communications offering an attractive way to perform TE-based path computation among cooperating PCEs in multi-layer/domain scenarios, while preserving scalability and confidentiality. This survey presents the state-of-the-art on the PCE architecture for GMPLS-controlled networks carried out by research and standardization community. In this work, packet (i.e., MPLS-TE and MPLS-TP) and wavelength/spectrum (i.e., WSON and SSON) switching capabilities are the considered technological platforms, in which the PCE is shown to achieve a number of evident benefits

Resource allocation and scalability in dynamic wavelength-routed optical networks.

This thesis investigates the potential benefits of dynamic operation of wavelength-routed optical networks (WRONs) compared to the static approach. It is widely believed that dynamic operation of WRONs would overcome the inefficiencies of the static allocation in improving resource use. By rapidly allocating resources only when and where required, dynamic networks could potentially provide the same service that static networks but at decreased cost, very attractive to network operators. This hypothesis, however, has not been verified. It is therefore the focus of this thesis to investigate whether dynamic operation of WRONs can save significant number of wavelengths compared to the static approach whilst maintaining acceptable levels of delay and scalability. Firstly, the wavelength-routed optical-burst-switching (WR-OBS) network architecture is selected as the dynamic architecture to be studied, due to its feasibility of implementation and its improved network performance. Then, the wavelength requirements of dynamic WR-OBS are evaluated by means of novel analysis and simulation and compared to that of static networks for uniform and non-uniform traffic demand. It is shown that dynamic WR-OBS saves wavelengths with respect to the static approach only at low loads and especially for sparsely connected networks and that wavelength conversion is a key capability to significantly increase the benefits of dynamic operation. The mean delay introduced by dynamic operation of WR-OBS is then assessed. The results show that the extra delay is not significant as to violate end-to-end limits of time-sensitive applications. Finally, the limiting scalability of WR-OBS as a function of the lightpath allocation algorithm computational complexity is studied. The trade-off between the request processing time and blocking probability is investigated and a new low-blocking and scalable lightpath allocation algorithm which improves the mentioned trade-off is proposed. The presented algorithms and results can be used in the analysis and design of dynamic WRONs

QoS Considerations in OBS Switched Backbone Net-Works

Optical Burst Switching (OBS) was proposed as a hybrid switching technology solution to handle the multi-Terabit volumes of traffic anticipated to traverse Future Generation backbone Networks. With OBS, incoming data packets are assembled into super-sized packets called data bursts and then assigned an end to end light path. Key challenging areas with regards to OBS Networks implementation are data bursts assembling and scheduling at the network ingress and core nodes respectively as they are key to minimizing subsequent losses due to contention among themselves in the core nodes. These losses are significant contributories to serious degradation in renderable QoS. The paper overviews existing methods of enhancing it at both burst and transport levels. A distributed resources control architecture is proposed together with a proposed wavelength assignment algorithm

Investigation of the tolerance of wavelength-routed optical networks to traffic load variations.

This thesis focuses on the performance of circuit-switched wavelength-routed optical network with unpredictable traffic pattern variations. This characteristic of optical networks is termed traffic forecast tolerance. First, the increasing volume and heterogeneous nature of data and voice traffic is discussed. The challenges in designing robust optical networks to handle unpredictable traffic statistics are described. Other work relating to the same research issues are discussed. A general methodology to quantify the traffic forecast tolerance of optical networks is presented. A traffic model is proposed to simulate dynamic, non-uniform loads, and used to test wavelength-routed optical networks considering numerous network topologies. The number of wavelengths required and the effect of the routing and wavelength allocation algorithm are investigated. A new method of quantifying the network tolerance is proposed, based on the calculation of the increase in the standard deviation of the blocking probabilities with increasing traffic load non-uniformity. The performance of different networks are calculated and compared. The relationship between physical features of the network topology and traffic forecast tolerance is investigated. A large number of randomly connected networks with different sizes were assessed. It is shown that the average lightpath length and the number of wavelengths required for full interconnection of the nodes in static operation both exhibit a strong correlation with the network tolerance, regardless of the degree of load non-uniformity. Finally, the impact of wavelength conversion on network tolerance is investigated. Wavelength conversion significantly increases the robustness of optical networks to unpredictable traffic variations. In particular, two sparse wavelength conversion schemes are compared and discussed: distributed wavelength conversion and localized wavelength conversion. It is found that the distributed wavelength conversion scheme outperforms localized wavelength conversion scheme, both with uniform loading and in terms of the network tolerance. The results described in this thesis can be used for the analysis and design of reliable WDM optical networks that are robust to future traffic demand variations