Synthesis Lectures on Learning, Networks, and Algorithms – serie

As the cybersecurity threat landscape continues to bring about novel forms of attacks and losses, ransomware insurance being the latest example, the insurance practice is also evolving in terms of what types of losses are covered, what are excluded, and how cyber insurance intersects with traditional casualty and property insurance.

This book provides a comprehensive treatment of the Poisson line Cox process (PLCP) and its applications to vehicular networks. The PLCP is constructed by placing points on each line of a Poisson line process (PLP) as per an independent Poisson point process (PPP).

With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviewsthe background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

This book provides a quick reference and insights into modeling and optimization of software-defined networks (SDNs). It covers various algorithms and approaches that have been developed for optimizations related to the control plane, the considerable research related to data plane optimization, and topics that have significant potential for research and advances to the state-of-the-art in SDN. Over the past ten years, network programmability has transitioned from research concepts to more mainstream technology through the advent of technologies amenable to programmability such as service chaining, virtual network functions, and programmability of the data plane. However, the rapid development in SDN technologies has been the key driver behind its evolution. The logically centralized abstraction of network states enabled by SDN facilitates programmability and use of sophisticated optimization and control algorithms for enhancing network performance, policy management, and security.Furthermore, the centralized aggregation of network telemetry facilitates use of data-driven machine learning-based methods. To fully unleash the power of this new SDN paradigm, though, various architectural design, deployment, and operations questions need to be addressed. Associated with these are various modeling, resource allocation, and optimization opportunities.The book covers these opportunities and associated challenges, which represent a ``call to arms'' for the SDN community to develop new modeling and optimization methods that will complement or improve on the current norms.

Moreover, the complexity of the connectivity would predominantly affect the hardness of connectivity optimization, which is a fundamental problem for network connectivity studies.

This book addresses the urgent issue of massive and inefficient energy consumption by data centers, which have become the largest co-located computing systems in the world and process trillions of megabytes of data every second. Dynamic provisioning algorithms have the potential to be the most viable and convenient of approaches to reducing data center energy consumption by turning off unnecessary servers, but they incur additional costs from being unable to properly predict future workload demands that have only recently been mitigated by advances in machine-learned predictions.This book explores whether it is possible to design effective online dynamic provisioning algorithms that require zero future workload information while still achieving close-to-optimal performance. It also examines whether characterizing the benefits of utilizing the future workload information can then improve the design of online algorithms with predictions in dynamic provisioning. The book specifically develops online dynamic provisioning algorithms with and without the available future workload information. Readers will discover the elegant structure of the online dynamic provisioning problem in a way that reveals the optimal solution through divide-and-conquer tactics. The book teaches readers to exploit this insight by showing the design of two online competitive algorithms with competitive ratios characterized by the normalized size of a look-ahead window in which exact workload prediction is available.

This book addresses the urgent issue of massive and inefficient energy consumption by data centers, which have become the largest co-located computing systems in the world and process trillions of megabytes of data every second. Dynamic provisioning algorithms have the potential to be the most viable and convenient of approaches to reducing data center energy consumption by turning off unnecessary servers, but they incur additional costs from being unable to properly predict future workload demands that have only recently been mitigated by advances in machine-learned predictions.This book explores whether it is possible to design effective online dynamic provisioning algorithms that require zero future workload information while still achieving close-to-optimal performance. It also examines whether characterizing the benefits of utilizing the future workload information can then improve the design of online algorithms with predictions in dynamic provisioning. The book specifically develops online dynamic provisioning algorithms with and without the available future workload information. Readers will discover the elegant structure of the online dynamic provisioning problem in a way that reveals the optimal solution through divide-and-conquer tactics. The book teaches readers to exploit this insight by showing the design of two online competitive algorithms with competitive ratios characterized by the normalized size of a look-ahead window in which exact workload prediction is available.

This book presents Internet transport economics as a new approach to understanding the packet-switching paradigm of Internet infrastructure as a global transport system for data packets.

This book presents Internet transport economics as a new approach to understanding the packet-switching paradigm of Internet infrastructure as a global transport system for data packets.

The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD.

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.

This book provides an accessible yet rigorous first reference for readers interested in learning how to model and analyze cellular network performance using stochastic geometry. In addition to the canonical downlink and uplink settings, analyses of heterogeneous cellular networks and dense cellular networks are also included. For each of these settings, the focus is on the calculation of coverage probability, which gives the complementary cumulative distribution function (ccdf) of signal-to-interference-and-noise ratio (SINR) and is the complement of the outage probability. Using this, other key performance metrics, such as the area spectral efficiency, are also derived. These metrics are especially useful in understanding the effect of densification on network performance. In order to make this a truly self-contained reference, all the required background material from stochastic geometry is introduced in a coherent and digestible manner.This Book:Provides an approachable introduction to the analysis of cellular networks and illuminates key system dependenciesFeatures an approach based on stochastic geometry as applied to cellular networks including both downlink and uplinkFocuses on the statistical distribution of signal-to-interference-and-noise ratio (SINR) and related metrics

This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research.

This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research.

This book provides an accessible yet rigorous first reference for readers interested in learning how to model and analyze cellular network performance using stochastic geometry. In addition to the canonical downlink and uplink settings, analyses of heterogeneous cellular networks and dense cellular networks are also included. For each of these settings, the focus is on the calculation of coverage probability, which gives the complementary cumulative distribution function (ccdf) of signal-to-interference-and-noise ratio (SINR) and is the complement of the outage probability. Using this, other key performance metrics, such as the area spectral efficiency, are also derived. These metrics are especially useful in understanding the effect of densification on network performance. In order to make this a truly self-contained reference, all the required background material from stochastic geometry is introduced in a coherent and digestible manner.This Book:Provides an approachable introduction to the analysis of cellular networks and illuminates key system dependenciesFeatures an approach based on stochastic geometry as applied to cellular networks including both downlink and uplinkFocuses on the statistical distribution of signal-to-interference-and-noise ratio (SINR) and related metrics

This book provides a comprehensive overview of reinforcement learning for ridesharing applications. The book describes the research problems associated with the various aspects of a ridesharing system and discusses the existing reinforcement learning approaches for solving them.

This book provides a comprehensive overview of reinforcement learning for ridesharing applications. The authors first lay out the fundamentals of the ridesharing system architectures and review the basics of reinforcement learning, including the major applicable algorithms. The book describes the research problems associated with the various aspects of a ridesharing system and discusses the existing reinforcement learning approaches for solving them. The authors survey the existing research on each problem, and then examine specific case studies. The book also includes a review of two of methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control.

This book presents algorithms and tools that are designed to model and extract information from personal contact networks, which represent which individuals in a population are physically in contact with one another.

Traditional network optimization focuses on a single control objective in a network populated by obedient users and limited dispersion of information.Table of Contents: Static Games and Solution Concepts / Game Theory Dynamics / Wireline Network Games / Wireless Network Games / Future Perspectives

Table of Contents: Introduction / NS-2 Simulator Preliminaries / How to work with trace files / Description and simulation of TCP/IP / Routing and network dynamics / RED: Random Early Discard / Differentiated Services / Mobile Networks and Wireless Local Area Networks / Classical queueing models / Tcl and C++ linkage

Packet delay and energy consumption are important considerations in wireless and sensor networks as these metrics directly affect the quality of service of the application and the resource consumption of the network;

Table of Contents: Preface / Introduction / A Study of the Base Case / Admission Control / Scheduling Policies / Utility Maximization without Rate Adaptation / Utility Maximization with Rate Adaptation / Systems with Both Real-Time Flows and Non-Real-Time Flows / Broadcasting and Network Coding / Bibliography / Authors' Biographies

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing.

The pricing models include social optimal pricing, monopoly pricing, price differentiation, oligopoly pricing, and network externalities, supported by introductory discussions of convex optimization and game theory.

Resource Allocation lies at the heart of network control. In the early days of the Internet the scarcest resource was bandwidth, but as the network has evolved to become an essential utility in the lives of billions, the nature of the resource allocation problem has changed.

Specifically, we introduce the tools and techniques needed to solve problems in PNC, and many of these tools and techniques are drawn from the more general disciplines of signal processing, communications, and networking: PNC is used as a pivot to learn about the fundamentals of signal processing techniques and wireless communications in general.

The last decade has seen an unprecedented growth in the demand for wireless services. These services are fueled by applications that often require not only high data rates, but also very low latency to function as desired. However, as wireless networks grow and support increasingly large numbers of users, these control algorithms must also incur only low complexity in order to be implemented in practice. Therefore, there is a pressing need to develop wireless control algorithms that can achieve both high throughput and low delay, but with low-complexity operations. While these three performance metrics, i.e., throughput, delay, and complexity, are widely acknowledged as being among the most important for modern wireless networks, existing approaches often have had to sacrifice a subset of them in order to optimize the others, leading to wireless resource allocation algorithms that either suffer poor performance or are difficult to implement. In contrast, the recent results presented inthis book demonstrate that, by cleverly taking advantage of multiple physical or virtual channels, one can develop new low-complexity algorithms that attain both provably high throughput and provably low delay. The book covers both the intra-cell and network-wide settings. In each case, after the pitfalls of existing approaches are examined, new systematic methodologies are provided to develop algorithms that perform provably well in all three dimensions.

The congestion control mechanism has been responsible for maintaining stability as the Internet scaled up by many orders of magnitude in size, speed, traffic volume, coverage, and complexity over the last three decades.

This book discusses an efficient random linear network coding scheme, called BATched Sparse code, or BATS code, which is proposed for communication through multi-hop networks with packet loss.