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Distributed Denial of Service (DDoS) Attacks: Classification, Attacks, Challenges and Countermeasures
by Brij B. Gupta Amrita DahiyaThe complexity and severity of the Distributed Denial of Service (DDoS) attacks are increasing day-by-day. The Internet has a highly inconsistent structure in terms of resource distribution. Numerous technical solutions are available, but those involving economic aspects have not been given much consideration. The book, DDoS Attacks – Classification, Attacks, Challenges, and Countermeasures, provides an overview of both types of defensive solutions proposed so far, exploring different dimensions that would mitigate the DDoS effectively and show the implications associated with them. Features: Covers topics that describe taxonomies of the DDoS attacks in detail, recent trends and classification of defensive mechanisms on the basis of deployment location, the types of defensive action, and the solutions offering economic incentives. Introduces chapters discussing the various types of DDoS attack associated with different layers of security, an attacker’s motivations, and the importance of incentives and liabilities in any defensive solution. Illustrates the role of fair resource-allocation schemes, separate payment mechanisms for attackers and legitimate users, negotiation models on cost and types of resources, and risk assessments and transfer mechanisms. DDoS Attacks – Classification, Attacks, Challenges, and Countermeasures is designed for the readers who have an interest in the cybersecurity domain, including students and researchers who are exploring different dimensions associated with the DDoS attack, developers and security professionals who are focusing on developing defensive schemes and applications for detecting or mitigating the DDoS attacks, and faculty members across different universities.
Distributed, Embedded and Real-time Java Systems
by M. Teresa Higuera-Toledano Andy J. WellingsResearch on real-time Java technology has been prolific over the past decade, leading to a large number of corresponding hardware and software solutions, and frameworks for distributed and embedded real-time Java systems. This book is aimed primarily at researchers in real-time embedded systems, particularly those who wish to understand the current state of the art in using Java in this domain. Much of the work in real-time distributed, embedded and real-time Java has focused on the Real-time Specification for Java (RTSJ) as the underlying base technology, and consequently many of the Chapters in this book address issues with, or solve problems using, this framework. Describes innovative techniques in: scheduling, memory management, quality of service and communication systems supporting real-time Java applications;Includes coverage of multiprocessor embedded systems and parallel programming;Discusses state-of-the-art resource management for embedded systems, including Java's real-time garbage collection and parallel collectors;Considers hardware support for the execution of Java programs including how programs can interact with functional accelerators;Includes coverage of Safety Critical Java for development of safety critical embedded systems.
Distributed Embedded Controller Development with Petri Nets
by Filipe Moutinho Luís GomesThis book describes a model-based development approach for globally-asynchronous locally-synchronous distributed embedded controllers. This approach uses Petri nets as modeling formalism to create platform and network independent models supporting the use of design automation tools. To support this development approach, the Petri nets class in use is extended with time-domains and asynchronous-channels. The authors' approach uses models not only providing a better understanding of the distributed controller and improving the communication among the stakeholders, but also to be ready to support the entire lifecycle, including the simulation, the verification (using model-checking tools), the implementation (relying on automatic code generators), and the deployment of the distributed controller into specific platforms. Uses a graphical and intuitive modeling formalism supported by design automation tools; Enables verification, ensuring that the distributed controller was correctly specified; Provides flexibility in the implementation and maintenance phases to achieve desired constraints (high performance, low power consumption, reduced costs), enabling porting to different platforms using different communication nodes, without changing the underlying behavioral model.
Distributed Embedded Smart Cameras
by Christophe Bobda Senem VelipasalarThis publication addresses distributed embedded smart cameras -cameras that perform on board analysis and collaborate with other cameras. This book provides the material required to better understand the architectural design challenges of embedded smart camera systems, the hardware/software ecosystem, the design approach for and applications of distributed smart cameras together with the state-of-the-art algorithms. The authors concentrate on the architecture, hardware/software design, realization of smart camera networks from applications to architectures, in particular in the embedded and mobile domains.
Distributed Fusion Estimation for Sensor Networks with Communication Constraints
by Wen-An Zhang Bo Chen Haiyu Song Li YuThisbook systematically presents energy-efficient robust fusion estimation methodsto achieve thorough and comprehensive results in the context of network-basedfusion estimation. It summarizes recent findings on fusion estimation withcommunication constraints; several novel energy-efficient and robust designmethods for dealing with energy constraints and network-induced uncertaintiesare presented, such as delays, packet losses, and asynchronous information. . . All the results are presented as algorithms, which are convenient for practicalapplications.
Distributed Game Development: Harnessing Global Talent to Create Winning Games
by Tim FieldsTake control of your global game development team and make successful AAA game titles using the 'Distributed Development' model. Game industry veteran Tim Fields teaches you how to evaluate game deals, how to staff teams for highly distributed game development, and how to maintain challenging relationships in order to get great games to market. This book is filled with interviews with a broad spectrum of industry experts from top game publishers and business owners in the US and UK. A supplementary web site provides interviews from the book, a forum where developers and publishers can connect, and additional tips and tricks. Topics include:
Distributed Geolibraries: Spatial Information Resources Summary of a Workshop
by National Research CouncilPresents the findings of the Workshop on Distributed Geolibraries: Spatial Information Resources, convened by the Mapping Science Committee of the National Research Council in June 1998
Distributed Graph Algorithms for Computer Networks
by K. ErciyesThis book presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Topics and features: introduces a range of fundamental graph algorithms, covering spanning trees, graph traversal algorithms, routing algorithms, and self-stabilization; reviews graph-theoretical distributed approximation algorithms with applications in ad hoc wireless networks; describes in detail the implementation of each algorithm, with extensive use of supporting examples, and discusses their concrete network applications; examines key graph-theoretical algorithm concepts, such as dominating sets, and parameters for mobility and energy levels of nodes in wireless ad hoc networks, and provides a contemporary survey of each topic; presents a simple simulator, developed to run distributed algorithms; provides practical exercises at the end of each chapter.
Distributed Hash Table
by Hao Zhang Yonggang Wen Haiyong Xie Nenghai YuThis SpringerBrief summarizes the development of Distributed Hash Table in both academic and industrial fields. It covers the main theory, platforms and applications of this key part in distributed systems and applications, especially in large-scale distributed environments. The authors teach the principles of several popular DHT platforms that can solve practical problems such as load balance, multiple replicas, consistency and latency. They also propose DHT-based applications including multicast, anycast, distributed file systems, search, storage, content delivery network, file sharing and communication. These platforms and applications are used in both academic and commercials fields, making Distributed Hash Table a valuable resource for researchers and industry professionals.
Distributed Heterogeneous Multi Sensor Task Allocation Systems (Automation, Collaboration, & E-Services #7)
by Itshak Tkach Yael EdanToday’s real-world problems and applications in sensory systems and target detection require efficient, comprehensive and fault-tolerant multi-sensor allocation. This book presents the theory and applications of novel methods developed for such sophisticated systems. It discusses the advances in multi-agent systems and AI along with collaborative control theory and tools. Further, it examines the formulation and development of an allocation framework for heterogeneous multi-sensor systems for various real-world problems that require sensors with different performances to allocate multiple tasks, with unknown a priori priorities that arrive at unknown locations at unknown time. It demonstrates how to decide which sensor to allocate to which tasks when and where. Lastly, it explains the reliability and availability issues of task allocation systems, and includes methods for their optimization.The presented methods are explained, measured, and evaluated by extensive simulations, and the results of these simulations are presented in this book. This book is an ideal resource for academics, researchers and graduate students as well as engineers and professionals and is relevant for various applications such as sensor network design, multi-agent systems, task allocation, target detection, and team formation.
Distributed Large-Scale Dimensional Metrology
by Domenico Maisano Fiorenzo Franceschini Luca Mastrogiacomo Barbara Pralio Maurizio GalettoThe field of large-scale dimensional metrology (LSM) deals with objects that have linear dimensions ranging from tens to hundreds of meters. It has recently attracted a great deal of interest in many areas of production, including the automotive, railway, and shipbuilding sectors. Distributed Large-Scale Dimensional Metrology introduces a new paradigm in this field that reverses the classical metrological approach: measuring systems that are portable and can be easily moved around the location of the measured object, which is preferable to moving the object itself. Distributed Large-Scale Dimensional Metrology combines the concepts of distributed systems and large scale metrology at the application level. It focuses on the latest insights and challenges of this new generation of systems from the perspective of the designers and developers. The main topics are: coverage of measuring area,sensors calibration,on-line diagnostics,probe management, andanalysis of metrological performance.The general descriptions of each topic are further enriched by specific examples concerning the use of commercially available systems or the development of new prototypes. This will be particularly useful for professional practitioners such as quality engineers, manufacturing and development engineers, and procurement specialists, but Distributed Large-Scale Dimensional Metrology also has a wealth of information for interested academics.
Distributed Learning: Social and Cultural Approaches to Practice
by Mary R. Lea Kathy NicollAt a time of increasing globalisation, the concept of open and distance learning is being constantly redefined. New technologies have opened up new ways of understanding and participating in Learning. Distributed Learning offers a collection of perspectives from a social and cultural practice-based viewpoint, with contributions from leading international authors in the field. Key issues in this comprehensive text are:*the challenges of ICT to traditional teaching and learning practices*the value and relevance of 'activity theory' and 'communities of practice' in educational institutions and the workplace*perspectives on the relationship between globalisation and distributed learning, and the breakdown of distinctions between global and local contexts*issues of identity and community in designing courses for the virtual student*language and literacies in distributed learning contextsThis book provides useful introductory reading, building a sound theoretical framework for practitioners interested in how distributed learning is shaping post-compulsory education.
Distributed Ledger Technology: 7th International Symposium, SDLT 2023, Brisbane, QLD, Australia, November 30 – December 1, 2023, Revised Selected Papers (Communications in Computer and Information Science #1975)
by Naipeng Dong Babu Pillai Guangdong Bai Mark UttingThis book constitutes the proceedings of the 7th International Symposium, SDLT 2023, held in Brisbane, QLD, Australia, during November 30 – December 1, 2023.The 8 full papers and the short paper included in this volume were carefully reviewed and selected from 32 submissions. The volume focuses on current systems and new solutions to create a scientific background for a solid development of innovative Distributed Ledger Technology application.
Distributed Ledgers: Design and Regulation of Financial Infrastructure and Payment Systems
by Robert M. TownsendAn economic analysis of what distributed ledgers can do, examining key components and discussing applications in both developed and emerging market economies.Distributed ledger technology (DLT) has the potential to transform economic organization and financial structure. In this book, Robert Townsend steps back from the hype and controversy surrounding DLT (and the related, but not synonymous, innovations of blockchain and Bitcoin) to offer an economic analysis of what distributed ledgers can do. Townsend examines the key components of distributed ledgers, discussing, evaluating, and illustrating each in the context of historical and contemporary economics, and reviewing featured applications in both developed economies and emerging-market countries.
A Distributed Linear Programming Models in a Smart Grid (Power Electronics and Power Systems)
by Prakash Ranganathan Kendall E. NygardThis book showcases the strengths of Linear Programming models for Cyber Physical Systems (CPS), such as the Smart Grids. Cyber-Physical Systems (CPS) consist of computational components interconnected by computer networks that monitor and control switched physical entities interconnected by physical infrastructures. A fundamental challenge in the design and analysis of CPS is the lack of understanding in formulating constraints for complex networks. We address this challenge by employing collection of Linear programming solvers that models the constraints of sub-systems and micro grids in a distributed fashion. The book can be treated as a useful resource to adaptively schedule resource transfers between nodes in a smart power grid. In addition, the feasibility conditions and constraints outlined in the book will enable in reaching optimal values that can help maintain the stability of both the computer network and the physical systems. It details the collection of optimization methods that are reliable for electric-utilities to use for resource scheduling, and optimizing their existing systems or sub-systems. The authors answer to key questions on ways to optimally allocate resources during outages, and contingency cases (e. g. , line failures, and/or circuit breaker failures), how to design de-centralized methods for carrying out tasks using decomposition models; and how to quantify un-certainty and make decisions in the event of grid failures.
Distributed Machine Learning and Computing: Theory and Applications (Big and Integrated Artificial Intelligence #2)
by M. Hadi AminiThis book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques.
Distributed Machine Learning and Gradient Optimization (Big Data Management)
by Jiawei Jiang Bin Cui Ce ZhangThis book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Distributed Machine Learning Patterns
by Yuan TangPractical patterns for scaling machine learning from your laptop to a distributed cluster.Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you&’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you&’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You&’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation
Distributed Machine Learning with PySpark: Migrating Effortlessly from Pandas and Scikit-Learn
by Abdelaziz TestasMigrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will Learn Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learn Who This Book Is For Data scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.
Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
by Guanhua WangBuild and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloudKey FeaturesAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook DescriptionReducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.What you will learnDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is forThis book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.
Distributed Medium Access Control in Wireless Networks
by Weihua Zhuang Ping WangThis brief investigates distributed medium access control (MAC) with QoS provisioning for both single- and multi-hop wireless networks including wireless local area networks (WLANs), wireless ad hoc networks, and wireless mesh networks. For WLANs, an efficient MAC scheme and a call admission control algorithm are presented to provide guaranteed QoS for voice traffic and, at the same time, increase the voice capacity significantly compared with the current WLAN standard. In addition, a novel token-based scheduling scheme is proposed to provide great flexibility and facility to the network service provider for service class management. Also proposed is a novel busy-tone based distributed MAC scheme for wireless ad hoc networks and a collision-free MAC scheme for wireless mesh networks, respectively, taking the different network characteristics into consideration. The proposed schemes enhance the QoS provisioning capability to real-time traffic and, at the same time, significantly improve the system throughput and fairness performance for data traffic, as compared with the most popular IEEE 802.11 MAC scheme.
Distributed Multimedia Database Technologies Supported by MPEG-7 and MPEG-21
by Harald KoschA multimedia system needs a mechanism to communicate with its environment, the Internet, clients, and applications. MPEG-7 provides a standard metadata format for global communication, but lacks the framework to let the various players in a system interact. MPEG-21 closes this gap by establishing an infrastructure for a distributed multimedia frame
Distributed Multiple Description Coding
by Yao Zhao Huihui Bai Jeng-Shyang Pan Ajith Abraham Anhong WangThis book examines distributed video coding (DVC) and multiple description coding (MDC), two novel techniques designed to address the problems of conventional image and video compression coding. Covering all fundamental concepts and core technologies, the chapters can also be read as independent and self-sufficient, describing each methodology in sufficient detail to enable readers to repeat the corresponding experiments easily. Topics and features: provides a broad overview of DVC and MDC, from the basic principles to the latest research; covers sub-sampling based MDC, quantization based MDC, transform based MDC, and FEC based MDC; discusses Sleplian-Wolf coding based on Turbo and LDPC respectively, and comparing relative performance; includes original algorithms of MDC and DVC; presents the basic frameworks and experimental results, to help readers improve the efficiency of MDC and DVC; introduces the classical DVC system for mobile communications, providing the developmental environment in detail.
Distributed .NET with Microsoft Orleans: Build robust and highly scalable distributed applications without worrying about complex programming patterns
by Bhupesh Guptha Muthiyalu Suneel Kumar KunaniAdopt an effortless approach to avoid the hassles of complex concurrency and scaling patterns when building distributed applications in .NETKey FeaturesExplore the Orleans cross-platform framework for building robust, scalable, and distributed applicationsHandle concurrency, fault tolerance, and resource management without complex programming patternsWork with essential components such as grains and silos to write scalable programs with easeBook DescriptionBuilding distributed applications in this modern era can be a tedious task as customers expect high availability, high performance, and improved resilience. With the help of this book, you'll discover how you can harness the power of Microsoft Orleans to build impressive distributed applications.Distributed .NET with Microsoft Orleans will demonstrate how to leverage Orleans to build highly scalable distributed applications step by step in the least possible time and with minimum effort. You'll explore some of the key concepts of Microsoft Orleans, including the Orleans programming model, runtime, virtual actors, hosting, and deployment. As you advance, you'll become well-versed with important Orleans assets such as grains, silos, timers, and persistence. Throughout the book, you'll create a distributed application by adding key components to the application as you progress through each chapter and explore them in detail.By the end of this book, you'll have developed the confidence and skills required to build distributed applications using Microsoft Orleans and deploy them in Microsoft Azure.What you will learnGet to grips with the different cloud architecture patterns that can be leveraged for building distributed applicationsManage state and build a custom storage providerExplore Orleans key design patterns and understand when to reuse themWork with different classes that are created by code generators in the Orleans frameworkWrite unit tests for Orleans grains and silos and create mocks for different parts of the systemOvercome traditional challenges of latency and scalability while building distributed applicationsWho this book is forThis book is for .NET developers and software architects looking for a simplified guide for creating distributed applications, without worrying about complex programming patterns. Intermediate web developers who want to build highly scalable distributed applications will also find this book useful. A basic understanding of .NET Classic or .NET Core with C# and Azure will be helpful.
Distributed Network Data: From Hardware to Data to Visualization
by Alasdair Allan Kipp BradfordBuild your own distributed sensor network to collect, analyze, and visualize real-time data about our human environment--including noise level, temperature, and people flow. With this hands-on book, you'll learn how to turn your project idea into working hardware, using the easy-to-learn Arduino microcontroller and off-the-shelf sensors. Authors Alasdair Allan and Kipp Bradford walk you through the entire process, from prototyping a simple sensor node to performing real-time analysis on data captured by a deployed multi-sensor network. Demonstrated at recent O'Reilly Strata Conferences, the future of distributed data is already here. If you have programming experience, you can get started immediately. Wire up a circuit on a breadboard, and use the Arduino to read values from a sensor Add a microphone and infrared motion detector to your circuit Move from breadboard to prototype with Fritzing, a program that converts your circuit design into a graphical representation Simplify your design: learn use cases and limitations for using Arduino pins for power and grounding Build wireless networks with XBee radios and request data from multiple sensor platforms Visualize data from your sensor network with Processing or LabVIEW