Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Optical burst switching (OBS) - a new paradigm for an optical Internet

Flexgrid technology is now considered to be a promising solution for future high-speed network design. In this context, we need a tutorial that covers the key aspects of elastic optical networks. This tutorial paper starts with a brief introduction of the elastic optical network and its unique characteristics. The paper then moves to the architecture of the elastic optical network and its operation principle. To complete the discussion of network architecture, this paper focuses on the different node architectures, and compares their performance in terms of scalability and flexibility. Thereafter, this paper reviews and classifies routing and spectrum allocation (RSA) approaches including their pros and cons. Furthermore, various aspects, namely, fragmentation, modulation, quality-of-transmission, traffic grooming, survivability, energy saving, and networking cost related to RSA, are presented. Finally, the paper explores the experimental demonstrations that have tested the functionality of the elastic optical network, and follows that with the research challenges and open issues posed by flexible networks.

Routing and Spectrum Allocation in Elastic Optical Networks: A Tutorial

The 5th generation mobile networks (5G) based on virtualized and centralized radio access networks will require cost-effective and flexible solutions for satisfying high-throughput and latency requirements. The next generation fronthaul interface (NGFI) architecture is one of the main candidates to achieve it. In the NGFI architecture, baseband processing is split and performed in radio (RU), distributed (DU), and central (CU) units. The mentioned entities are virtualized and performed on general-purpose processors forming a processing pool (PP) facility. Given that the location of PPs may be spread over the network and the PPs have limited capacity, it leads to the optimization problem concerning the placement of DUs and CUs. In the NGFI network scenario, the radio data between the RU, DU, CU, and a data center (DC)—in which the traffic is aggregated—are transmitted in the form of packets over a convergent packet-switched network. Because the packet transmission is nondeterministic, special attention should be put on ensuring the appropriate quality of service (QoS) levels for the latency-sensitive traffic flows. In this paper, we address the latency-aware DU and CU placement (LDCP) problem in NGFI. LDCP concerns the placement of DU/CU entities in PP nodes for a given set of demands assuming the QoS requirements of traffic flows that are related to their latency. To this end, we make use of mixed integer linear programming (MILP) in order to formulate the LDCP optimization problem and to solve it. To assure that the latency requirements are satisfied, we apply a reliable latency model, which is included in the MILP model as a set of constraints. To assess the effectiveness of the MILP method and analyze the network performance, we run a broad set of experiments in different network scenarios.

Latency-Aware DU/CU Placement in Convergent Packet-Based 5G Fronthaul Transport Networks

Most research works in optical burst switching (OBS) networks do not take into account the impact of physical layer impairments (PLIs) either by considering fully transparent (i.e., using optical 3R regeneration) or opaque (i.e., electrical 3R regeneration) networks. However, both solutions are not feasible for different reasons. In this paper, we propose a novel translucent OBS network architecture which aims at bridging the gap between the transparent and opaque solutions. In order to evaluate its performance, two different joint regenerator placement and routing heuristics are provided. Simulation results show that our translucent network model achieves performance results as good as those obtained with an opaque solution but with considerably less regenerators.

/pdf/modelling-and-performance-evaluation-of-a-translucent-obs-4270baa4na.pdf

Modelling and Performance Evaluation of a Translucent OBS Network Architecture

L'evolucio de les xarxes publiques de transport de dades destaca per el continu augment de la demanda de trafic a la que estan sotmeses. La causa es la imparable popularitzacio d'Internet i del seu us per a tot tipus d'aplicacions. Les xarxes de commutacio de rafegues optiques (OBS: Optical Bursts Switching) son una solucio extraordinariament prometedora per la proxima generacio de xarxes, tant per la flexibilitat que ofereixen com per el seu alt rendiment fruit de l'explotacio de la multiplexacio estadistica en el domini optic.Aquesta tesi presenta l'analisi, modelitzacio i avaluacio de les xarxes de commutacio de rafegues optiques basades en l'emulacio del temps de compensacio (emulated offset time: E-OBS). El concepte d'E-OBS defineix una arquitectura de xarxa OBS per al transportar i commutar rafegues optiques en una xarxa troncal en la que, al contrari de l'arquitectura convencional (C-OBS) en la que el temps de compensacio s'introdueix des dels nodes d'acces, el temps de compensacio s'introdueix en cadascun dels nodes de la xarxa per mitja d'un retardador de fibra addicional. L'arquitectura E-OBS permet superar algunes de les desavantatges inherents a arquitectures C-OBS, pero la seva gran virtut es la compatibilitat amb les xarxes de commutacio de circuits optics (OCS: Optical Circuit Switching) actuals i les futures xarxes de commutacio de paquets optics (OPS: Optical Packet Switching), de manera que les xarxes OBS basades en una arquitectura E-OBS) poden facilitat enormement la transicio de unes a les altres.A ala vista dels principals requeriments de disseny de les xarxes OBS, que son la resolucio de contencions en el domini optic, la provisio de qualitat de servei (QoS) i l'optim encaminament de les rafegues per tal de minimitzar la congestio de la xarxa, . en aquesta tesi es proposa un disseny de l'arquitectura E-OBS basada en (i) un metode viable per a la provisio del temps de compensacio, (ii) una qualitat alta global de servei, i (iii) un mecanisme d'encaminament que minimitzi congestio de xarxa.- La primera part d'aquesta tesi proporciona la informacio documental necessaria per al disseny d'E-OBS.- La segona part se centra en l'estudi de la funcionalitat i viabilitat de l'arquitectura E-OBS. S'introdueixen els principis d'operacio d'E-OBS i s'identifiquen els principals esculls que presenten les arquitectures C-OBS i que deixen de ser-ho en una arquitectura E-OBS. Alguns d'aquests esculls son la dificultat d'utilitzar un algorisme d'encaminament amb rutes alternatives, la complexitat dels algoritmes de reserva de recursos i la seva falta d'equitat, la complexitat en la provisio de la QoS, etc. En aquesta segona part es constata que l'arquitectura E-OBS redueix la complexitat dels de reserva de recursos i es verifica la viabilitat d'operacio i de funcionament de la provisio del tremps de compensacio en aquesta arquitectura a partir de figures de comportament obtingudes amb retardadors de fibra comercialment disponibles.- La tercera part encara el problema de la provisio de la QoS. Primer s'hi revisen els conceptes basics de QoS aixi com els mecanismes de tractament de la QoS per a xarxes OBS fent-ne una comparacio qualitativa i de rendiment de tots ells. Com a resultat s'obte que el mecanisme que presenta un millor comportament es el d'avortament de la transmissio de les rafegues de mes baixa prioritat quan aquestes col·lisionen amb una de prioritat mes alta (es l'anomenat Burst Preemption mechanism), el qual en alguns casos presenta un problema de senyalitzacio innecessaria. Aquesta tercera part es conclou amb la proposta d'un mecanisme de finestra a afegir al esquema de Burst Preemption que nomes funciona sobre una arquitectura E-OBS i que soluciona aquest problema.- En la quarta part s'afronta el problema de l'encaminament en xarxes OBS. S'estudia el comportament dels algoritmes d'encaminament adaptatius, els aillats amb rutes alternatives i els multicami distribuits, sobre xarxes E-OBS. A la vista dels resultats no massa satisfactoris que s'obtenen, es planteja una solucio alternativa que es basa en model d'optimitzacio no lineal. Es formulen i resolen dos models d'optimitzacio per als algoritmes encaminament de font multicami que redueixen notablement la congestio en les xarxes OBS.Finalment, aquesta tesi conclou que l'arquitectura E-OBS es factible, que es mes eficient que la C-OBS, que proveeix eficacment QoS, i que es capac d'operar amb diverses estrategies d'encaminament i de reduir eficacment la congestio de xarxa.

Offset time-emulated architecture for optical burst switching-modelling and performance evaluation

In this paper, we focus on dynamic lightpath provisioning in translucent elastic optical networks (EONs) realizing super-channel transmission in which flexible signal regeneration achieved with transponders operating in back-to-back (B2B) configurations and modulation conversion is allowed. We study potential power consumption savings and performance gains in terms of bandwidth blocking probability in such flexible network scenarios. To this end, we analyze two alternative scenarios that differ in the way in which translucent lightpath connections are provisioned, namely, a reference one in which the use of regeneration is minimized and the one with flexible B2B regeneration. For comparing these scenarios, we propose two dynamic routing strategies, one aiming at the minimization of bandwidth blocking probability and the second dedicated to minimization of power consumption. The results of simulation experiments run on two representative network topologies show that flexible signal regeneration can be beneficial in terms of both analyzed metrics.

Energy Efficiency of Dynamic Routing in Elastic Optical Networks with Back-to-Back Regeneration

This paper addresses the problem of routing optimization in optical burst switching (OBS) networks. We use a simplified anlytical model of OBS network with an overall burst loss probability as the primary metric of interest. Since the abjective function of optimization problem is nonlinear we propose two solutions based on a non-reduced link load (NR-LL) model and a reduced link load (R-LL) model. In order to find partial derivatives of the cost function we apply a calculation considered previously for circuit-switched networks. We derive exact partial derivatives of NR-LL model and we approximate the parial derivatives of R-LL model. Simulation results demonstrate that our solutions effectively reduce the overall burst loss probability over the shortest path routing. Moreover, in many cases. they over-perform an alternative routing.

/pdf/routing-optimization-in-optical-burst-switching-networks-32ny2guk9p.pdf

Miroslaw Klinkowski

Papers

Latency-Aware DU/CU Placement in Convergent Packet-Based 5G Fronthaul Transport Networks

Modelling and Performance Evaluation of a Translucent OBS Network Architecture

Offset time-emulated architecture for optical burst switching-modelling and performance evaluation

Energy Efficiency of Dynamic Routing in Elastic Optical Networks with Back-to-Back Regeneration

Routing optimization in optical burst switching networks