Resource allocation and performance analysis problems in optical networks

Optical networks pose a rich variety of new design and performance analysis problems. Typically, the static design problems belong to the field of combinatorial optimisation, whereas decision-making and performance analysis problems are best treated using appropriate stochastic models. This dissertation focuses on certain issues in resource allocation and performance evaluation of backbone wavelength-routed (WR) networks and metropolitan area optical burst switching (OBS) networks. The first two parts of the thesis consider heuristic algorithms for the static routing and wavelength assignment (RWA) and logical topology design (LTD) problems that arise in the context of WR networks. In a static RWA problem, one is asked to establish a given set of lightpaths (or light trees) in an optical WR network with given constraints, where the objective often is to minimise the number of wavelength channels required. In LTD problem, the number of wavelength channels is given and one is asked to decide on the set of lightpaths so that, for instance, the mean sojourn time of packets travelling at the logical layer is minimised. In the thesis, several heuristic algorithms for both the RWA and LTD problems are described and numerical results are presented. The third part of the thesis studies the dynamic control problem where connection requests, i.e. lightpath requests, arrive according to a certain traffic pattern and the task is to establish one lightpath at a time in the WR optical network so that the expected revenue is maximised or the expected cost is minimised. Typically, the goal of optimisation is to minimise some infinite time horizon cost function, such as the blocking probability. In this thesis, the dynamic RWA problem is studied in the framework of Markov decision processes (MDP). An algorithmic approach is proposed by which any given heuristic algorithm can be improved by applying the so-called first policy iteration (FPI) step of the MDP theory. Relative costs of states needed in FPI are estimated by on-the-fly simulations. The computational burden of the approach is alleviated by introducing the importance sampling (IS) technique with FPI, for which an adaptive algorithm is proposed for adjusting the optimal IS parameters at the same time as data are collected for the decision-making analysis. The last part of the thesis considers OBS networks, which represent an intermediate step towards full optical packet switching networks. In OBS networks, the data are transferred using optical bursts consisting of several IP packets going to the same destination. On the route of the burst, temporary reservations are made only for the time during which the burst is transmitted. This thesis focuses on fairness issues in OBS networks. It is demonstrated that fairness can be improved by using fibre delay lines together with Just-Enough-Time protocol (JET). Furthermore, by choosing the routes in an appropriate way one can also reach a satisfactory level of fairness and, at the same time, lower the overall blocking probability. Possible scheduling policies for metropolitan area OBS ring networks are also studied.reviewe

On QoS-assured degraded provisioning in service-differentiated multi-layer elastic optical networks

The emergence of new network applications is driving network operators to not only fulfill dynamic bandwidth requirements, but offer various grades of service. Degraded provisioning provides an effective solution to flexibly allocate resources in various dimensions to reduce blocking for differentiated demands when network congestion occurs. In this work, we investigate the novel problem of online degraded provisioning in service-differentiated multi-layer networks with optical elasticity. Quality of Service (QoS) is assured by service-holding-time prolongation and immediate access as soon as the service arrives without set-up delay. We decompose the problem into degraded routing and degraded resource allocation stages, and design polynomial-time algorithms with the enhanced multi-layer architecture to increase the network flexibility in temporal and spectral dimensions. Illustrative results verify that we can achieve significant reduction of network service failures, especially for requests with higher priorities. The results also indicate that degradation in optical layer can increase the network capacity, while the degradation in electric layer provides flexible time-bandwidth exchange.Comment: accepted by IEEE GLOBECOM 201

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

Wireless Communications in the Era of Big Data

The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications Magazin

Scalable dimensioning of resilient Lambda Grids

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