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

Optical wavelength-routed IP/MPLS networks could be a valid solution in the short time. Optical burst switching may represent an interesting method in the mid/long term. Optical packet switching can be seen as the last step of such an evolutionary path. To achieve satisfactory performance, significant efforts are required in developing key optical devices and components such as semiconductor optical amplifiers, optical gates and space switches, wavelength converters, fast tunable lasers and filters, and wavelength routers. Also some breakthroughs are needed in order to realize mature optical devices which allow dynamic buffering of optical packets.

Development of IP/WDM optical networks

In this paper, we focus on cloud-ready elastic optical network with distance-adaptive transponders. We analyze the trade-off between number of required regenerators and spectrum usage for a US backbone network with traffic patterns based on Cisco predictions.

On the regenerators usage in cloud-ready elastic optical networks with distance-adaptive modulation formats

The ever increasing IP traffic volume has finally brought to light the high inefficiency of current wavelength-routed rigid-grid networks in matching the client layer requirements. Such an issue results in the deployment of large-size, expensive and power-consuming IP/MPLS layers to perform the required grooming/aggregation functionality. To deal with this problem, the emerging flexgrid technology, allowing for reduced size frequency grids, usually referred to as frequency slot, has recently attracted great attention among network operators, component and equipment suppliers, and the research community. In this paper, we report the main contributions performed in the context of EU STRONGEST project regarding flexgrid optical networks.

/pdf/on-the-performance-of-flexgrid-based-optical-networks-2i7g3l8o67.pdf

On the performance of flexgrid-based optical networks

We present some issues related to QoS guarantee in next generation terabit IP optical networks. We report the basics of MPLS/MPLambdaS technologies and some of their functionalities that enable a QoS protection environment for IP networks. An application example of MPLS/MPLambdaS in photonic packet switching and wavelength switching to provide QoS is also presented.

QoS guarantees in IP optical networks using MPLS/MPLambdaS

The impact of modulation formats transmission reach on the spectrum usage in Elastic Optical Networks is studied. Results of extensive simulations run on representative network topologies are presented and discussed.

Miroslaw Klinkowski

Papers

Development of IP/WDM optical networks

On the regenerators usage in cloud-ready elastic optical networks with distance-adaptive modulation formats

On the performance of flexgrid-based optical networks

QoS guarantees in IP optical networks using MPLS/MPLambdaS

On the Impact of Modulation Format Transmission Reach on Spectrum Usage in Elastic Optical Networks