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

In this paper, we analyze the problem of transceiver sharing in survivable spectrally-spatially flexible optical networks (SS-FONs) that realize transmission of spectral super-channels and where flexible signal regeneration is provided by transceivers operating in back-to-back (B2B) configurations. Namely, to improve network throughput, we propose to use a backup transceiver sharing approach for protection of lightpaths provisioned in the network. To address the considered problem, we develop an Adaptive Survivable Routing with Back-to-Back Regeneration with Transceiver Sharing (ASRBR-TS) algorithm that serves dynamic routing requests protected by backup paths. The ASRBR-TS algorithm accounts for limited spectrum and transceiver resources and makes use of the flexibility of the B2B regeneration. With the use of ASRBR-TS, we examine potential performance gains of transceiver sharing in terms of bandwidth blocking probability (BBP) and the traffic load that can be provisioned in the network with 1% BBP threshold. The numerical experiments are run for two representative network topologies with realistic assumptions concerning applied physical model. In the experiments, we also analyze a squeezed protection approach, in which the backup path supports only a part of the bit-rate realized on the working path. The results show that the transceiver sharing approach can significantly improve the network performance, i.e., it allows to provision up to 49% more traffic when compared with a dedicated transceiver approach.

Transceiver Sharing in Survivable Spectrally-Spatially Flexible Optical Networks

Recently, the Content Oriented Networking (CON) has become a recognized approach that is gaining extensive acceptance and deployment. At the same time, Elastic Optical Networks (EONs) are considered as a very promising approach for effective bandwidth allocation in optical networks. In this paper, we consider various approaches to content distribution in EON protected by the Dedicated Path Protection (DPP) scheme. We evaluate impact of these approaches on the performance of survivable EON with respect to CAPEX cost, power consumption and spectrum usage. We run numerical experiments on two representative network topologies with traffic patterns based on the Cisco predictions. The results show that the additional overhead of using the DPP protection in the EON for the analyzed performance metrics is from 2.1 to 3.5, depending on the metric and considered content delivery scenario. Moreover, the obtained results prove that the content delivery scenario has a large impact on the performance of the EON used in the physical layer and its suitable choice can bring up to 50% savings in all considered metrics.

Content distribution in Elastic Optical Networks with Dedicated Path Protection

For a multi-core fiber (MCF) optical network, we develop an efficient approach to schedule simultaneous lightpath demands onto non-adjacent cores for minimum inter-core crosstalk. Simulation results show the effectiveness of the proposed approach.

Minimizing Inter-Core Crosstalk Jointly in Spatial, Frequency, and Time Domains for Scheduled Lightpath Demands in a Multi-Core Fiber Optical Network

https://www.cba.upc.edu/downloads/category/11-articles?download=504:[ARTICLE]Performance_overview_of_the_quasi-synchronous_operation_mode_in_OBS_networks.pdf

Performance overview of the quasi-synchronous operation mode in optical burst switching (OBS) networks

This paper addresses the problem of controlling the performance of optical burst switching (OBS) networks in presence of non-stationary traffic demands. We propose a joint burst admission control and forwarding mechanism that operates in core nodes. This mechanism dynamically adapts its behaviour according to the feedback messages received from other nodes. By not forwarding certain bursts not complying with given requirements, an admission control is implicitly made. Moreover, by forwarding bursts to appropriately selected nodes, traffic balancing is achieved. The advantageous effects of the proposed mechanism can additionally be amplified by granting extra offset time to the burst. The benefits provided by this mechanism are supported by numerical results.

/pdf/adaptive-burst-admission-and-forwarding-in-obs-networks-1rei76jm3h.pdf

Miroslaw Klinkowski

Papers

Transceiver Sharing in Survivable Spectrally-Spatially Flexible Optical Networks

Content distribution in Elastic Optical Networks with Dedicated Path Protection

Minimizing Inter-Core Crosstalk Jointly in Spatial, Frequency, and Time Domains for Scheduled Lightpath Demands in a Multi-Core Fiber Optical Network

Performance overview of the quasi-synchronous operation mode in optical burst switching (OBS) networks

Adaptive burst admission and forwarding in OBS networks