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

Packet-optical xHaul transport networks are a promising solution for assuring low-cost connectivity between a large number of antennas, located at remote sites, and a central site (hub), in which traffic is gathered and baseband processing is performed, in dense 5G radio access networks (RANs). The demand for fiber connections in such networks can be reduced by means of optical add-drop multiplexers (OADMs), which allow to aggregate traffic from a number of remote sites onto a single optical transmission path. An important issue here is the protection of transmission paths since the failure of a single link may result in the loss of connectivity of a number of antennas. In this work, we focus on the problem of planning a survivable 5G packet-optical xHaul access network, in which the remote sites are connected with the hub by means of disjoint primary-backup paths. The network scenario is based on a passive wavelength division multiplexing (WDM) system in which the maximum transmission distance depends on the number of OADMs installed on the transmission path. In the solution proposed, network survivability is achieved using a dedicated path protection with wavelength aggregation (DPPWA) mechanism. The network planning problem is modeled and solved as an integer linear programming (ILP) problem. Numerical results indicate, among others, that cost savings of between 20%–40% can be achieved in lower traffic load scenarios due to the traffic aggregation by means of OADMs, and that the savings increase with the cost of dark fiber lease.

Dedicated Path Protection With Wavelength Aggregation in 5G Packet-Optical Xhaul Access Networks

The research agenda in optical burst switching in e-Photon/ONe (Tutorial)

Closed-form analytical expressions for nonlinear noise power FWM density (IFWM) have been derived for Nyquist WDM systems as a function of the optical fiber and system parameters (i.e. dispersion, nonlinearity, power spectral density, and spectral efficiency) in case of near-zero fiber attenuation.

https://ieeexplore.ieee.org/iel7/6589037/6602671/06602881.pdf

Analytical modeling of FWM noise power in raman amplified transmission links

The deployment of translucent optical networks is considered the most promising short term solution to decrease costs and energy consumption in optical backbone networks. Indeed, due to the technological maturity of translucent wavelength switched optical network (WSON) architectures, they have already caught close attention from the research community. Moreover, recent advances and enhancements in optical devices are now (re-)fostering research interest in sub-wavelength technologies like, among others, optical burst switching (OBS) and optical packet switching (OPS). Hence, in this paper, we evaluate and compare two novel node architectures for a translucent OBS (T-OBS) network. To be precise, we study nodes with both dedicated and shared wavelength converter resources (i.e., DWC and SWC). To this end, we consider the impact of the main physical layer impairments (PLIs) and make use of a routing and regenerator placement and dimensioning (RRPD) algorithm to minimize the number of optical-electrical-optical (O/E/O) regenerators deployed in the network whilst, at the same time, guaranteeing a target quality of transmission (QoT) network performance. The results presented prove the feasibility and the significant savings, in terms of the number of wavelength converters (WCs), that can be achieved by considering a network with shared wavelength converter resources.

Translucent OBS network architectures with dedicated and shared wavelength converters

Space division multiplexing (SDM) seems to be a promising solution with the scaling potential to overcome the possible capacity crunch problem in optical backbone networks. The main goal of this paper is to provide a detailed analysis of three SDM scenarios, namely, SDM-Fixed, SDM-Single, and SDM-Flexible in terms of the spectrum usage and the wastage of spectral resources due to guardbands and resource overprovisioning.

Miroslaw Klinkowski

Papers

Dedicated Path Protection With Wavelength Aggregation in 5G Packet-Optical Xhaul Access Networks

The research agenda in optical burst switching in e-Photon/ONe (Tutorial)

Analytical modeling of FWM noise power in raman amplified transmission links

Translucent OBS network architectures with dedicated and shared wavelength converters

Spectrum usage for various SDM scenarios