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

European Railway Comparisons: Final Report

The Institute for Transport Studies (ITS), University of Leeds and the British Railways Board (BRB) carried out a major comparative study of Western European railways in the late 1970s (BRB and University of Leeds, 1979). Follow-up work was carried out by ITS financed by the Social Science Research Council and reported by Nash (1985). It was deaded to revive this work at ITS for a number of reasons: It is over ten years since the last set of comparisons (for 1981) were made at ITS and therefore a review of the changes in costs and productivity may be timely. There has been a number of technical developments that make the use of statistical cost analysis more promising. These developments include the use of more flexible functional forms such as the translog, and the development of comprehensive total factor productivity indices (see, for example, Dodgson, 1985 and, more recently, Hensher and Waters, 1993). There is increasing interest in the organisational structure of railway industries as a result of the 1988 Transport Act in Sweden, the EC directive 91/4-40 and the publication of proposals for privatising British Rail in July 1992 (see, for example, ECMT, 1993). Given the explosion in information technology, there were some hopes that data availability would have improved. (Continues..

Alarm-Based Prescriptive Process Monitoring

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs

Transport of video over partial order connections

A Partial Order and partial reliable Connection (POC) is an end-to-end transport connection authorized to deliver objects in an order that can differ from the transmitted one. Such a connection is also authorized to lose some objects. The POC concept is motivated by the fact that heterogeneous best-effort networks such as Internet are plagued by unordered delivery of packets and losses, which tax the performances of current applications and protocols. It has been shown, in several research works, that out of order delivery is able to alleviate (with respect to CO service) the use of end systems’ communication resources. In this paper, the efficiency of out-of-sequence delivery on MPEG video streams processing is studied. Firstly, the transport constraints (in terms of order and reliability) that can be relaxed by MPEG video decoders, for improving video transport, are detailed. Then, we analyze the performance gain induced by this approach in terms of blocking times and recovered errors. We demonstrate that POC connections fill not only the conceptual gap between TCP and UDP but also provide real performance improvements for the transport of multimedia streams such MPEG video

Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, “Improved Data Background to Support Current and Future Infrastructure Charging Systems”

Improverail aims are to further support the establishment of railway infrastructure management in accordance with Directive 91/440, as well as the new railway infrastructure directives, by developing the necessary tools for modelling the management of railway infrastructure; by evaluating improved methods for capacity and resources management, which allow the improvement of the Life Cycle Costs (LCC) calculating methods, including elements related to vehicle - infrastructure interaction and external costs; and by improving data background in support of charging for use of railway infrastructure. To achieve these objectives, Improverail is organised along 8 workpackages, with specific objectives, responding to the requirements of the task 2.2.1/10 of the 2nd call made in the 5th RTD Framework Programme in December 1999.This part is the task 7.1 (Review of infrastructure charging systems) to the workpackage 7 (Analysis of the relation between infrastructure cost variation and diversity of infrastructure charging systems).Before explaining the economic characteristics of railway and his basic pricing principles, authors must specify the objectives of railways infrastructure charging.principle of pricing ; rail infrastructure charging ; public service obligation ; rail charging practice ; Europe ; Improverail