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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 Ping Wang Weihua ZhuangThis 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 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 sensorAdd a microphone and infrared motion detector to your circuitMove from breadboard to prototype with Fritzing, a program that converts your circuit design into a graphical representationSimplify your design: learn use cases and limitations for using Arduino pins for power and groundingBuild wireless networks with XBee radios and request data from multiple sensor platformsVisualize data from your sensor network with Processing or LabVIEW
Distributed Networks: Intelligence, Security, and Applications
by Qurban A. MemonFor many civilian, security, and military applications, distributed and networked coordination offers a more promising alternative to centralized command and control in terms of scalability, flexibility, and robustness. It also introduces its own challenges. Distributed Networks: Intelligence, Security, and Applications brings together scientific research in distributed network intelligence, security, and novel applications. The book presents recent trends and advances in the theory and applications of network intelligence and helps you understand how to successfully incorporate them into distributed systems and services. Featuring contributions by leading scholars and experts from around the world, this collection covers: Approaches for distributed network intelligence Distributed models for distributed enterprises, including forecasting and performance measurement models Security applications for distributed enterprises, including intrusion tackling and peer-to-peer traffic detection Future wireless networking scenarios, including the use of software sensors instead of hardware sensors Emerging enterprise applications and trends such as the smartOR standard and innovative concepts for human–machine interaction in the operating room Several chapters use a tutorial style to emphasize the development process behind complex distributed networked systems and services, which highlights the difficulties of knowledge engineering of such systems. Delving into novel concepts, theories, and advanced technologies, this book offers inspiration for further research and development in distributed computing and networking, especially related to security solutions for distributed environments.
Distributed Optimization in Networked Systems: Algorithms and Applications (Wireless Networks)
by Xiaofeng Liao Huaqing Li Qingguo Lü Shaojiang Deng Shanfu GaoThis book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.
Distributed Optimization: Advances in Theories, Methods, and Applications
by Zheng Wang Tingwen Huang Xiaofeng Liao Huaqing Li Qingguo LüThis book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, from a communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.
Distributed Parameter Modeling and Boundary Control of Flexible Manipulators
by Wei He Jinkun LiuThe book investigates fundamental issues in flexible manipulator systems, including distributed parameter modeling and boundary controller design. It presents theoretical explorations of several fundamental problems concerning the dynamics and control of these systems. By integrating fresh concepts and results to form a systematic approach to control, it also provides a basic theoretical framework.In turn, the book offers a comprehensive treatment of flexible manipulator systems, addressing topics ranging from related distributed parameter modeling and advanced boundary controller design for these systems with input constraint, to active control with output constraint.In brief, the book addresses dynamical analysis and control design for flexible manipulator systems. Though primarily intended for researchers and engineers in the control system and mechanical engineering community, it can also serve as supplemental reading on the modeling and control of flexible manipulator systems at the postgraduate level.
Distributed Perception: Resonances and Axiologies (Routledge Studies in Science, Technology and Society)
by Natasha LushetichContributors to this book include key theorists and practitioners from media theory, Native Science, bio-media and sound art, philosophy, art history and design informatics. Collectively, they examine the becoming-technique of animal-human- machinic perceptibilities; and micro-perceptions that lie beneath the threshold of known perceptions yet create energetic vibrations. Who, what, and where perceives, and how? What are the sedimentations, inscriptions and axiologies of animal, human and machinic perceptions? What are their perceptibilities? Deleuze uses the word ‘visibilities’ to indicate that visual perception isn’t just a physiological given but cues operations productive of new assemblages. Perceptibilities are, by analogy, spatio- temporal, geolocative, kinaesthetic, audio-visual, and haptic operations that are always already memory. In the case of strong inscriptions, they are also epigenetic events. The contributors show distributed perception to be a key notion in addressing the emergence and persistence of plant, animal, human and machine relations. An invaluable reference for students and scholars in a range of areas including Media Theory, Sociology, Philosophy, Art and Design.
Distributed Programming
by A. Udaya ShankarDistributed Programming: Theory and Practice presents a practical and rigorous method to develop distributed programs that correctly implement their specifications. The method also covers how to write specifications and how to use them. Numerous examples such as bounded buffers, distributed locks, message-passing services, and distributed termination detection illustrate the method. Larger examples include data transfer protocols, distributed shared memory, and TCP network sockets. Distributed Programming: Theory and Practice bridges the gap between books that focus on specific concurrent programming languages and books that focus on distributed algorithms. Programs are written in a "real-life" programming notation, along the lines of Java and Python with explicit instantiation of threads and programs. Students and programmers will see these as programs and not "merely" algorithms in pseudo-code. The programs implement interesting algorithms and solve problems that are large enough to serve as projects in programming classes and software engineering classes. Exercises and examples are included at the end of each chapter with on-line access to the solutions. Distributed Programming: Theory and Practice is designed as an advanced-level text book for students in computer science and electrical engineering. Programmers, software engineers and researchers working in this field will also find this book useful.
Distributed Real-Time Architecture for Mixed-Criticality Systems
by Hamidreza Ahmadian Roman Obermaisser Jon PerezThis book describes a cross-domain architecture and design tools for networked complex systems where application subsystems of different criticality coexist and interact on networked multi-core chips. The architecture leverages multi-core platforms for a hierarchical system perspective of mixed-criticality applications. This system perspective is realized by virtualization to establish security, safety and real-time performance. The impact further includes a reduction of time-to-market, decreased development, deployment and maintenance cost, and the exploitation of the economies of scale through cross-domain components and tools. Describes an end-to-end architecture for hypervisor-level, chip-level, and cluster level. Offers a solution for different types of resources including processors, on-chip communication, off-chip communication, and I/O. Provides a cross-domain approach with examples for wind-power, health-care, and avionics. Introduces hierarchical adaptation strategies for mixed-criticality systems Provides modular verification and certification methods for the seamless integration of mixed-criticality systems. Covers platform technologies, along with a methodology for the development process. Presents an experimental evaluation of technological results in cooperation with industrial partners. The information in this book will be extremely useful to industry leaders who design and manufacture products with distributed embedded systems in mixed-criticality use-cases. It will also benefit suppliers of embedded components or development tools used in this area. As an educational tool, this material can be used to teach students and working professionals in areas including embedded systems, computer networks, system architecture, dependability, real-time systems, and avionics, wind-power and health-care systems.
Distributed Real-Time Systems: Theory and Practice (Computer Communications and Networks)
by K. ErciyesThis classroom-tested textbook describes the design and implementation of software for distributed real-time systems, using a bottom-up approach. The text addresses common challenges faced in software projects involving real-time systems, and presents a novel method for simply and effectively performing all of the software engineering steps. Each chapter opens with a discussion of the core concepts, together with a review of the relevant methods and available software. This is then followed with a description of the implementation of the concepts in a sample kernel, complete with executable code.Topics and features: introduces the fundamentals of real-time systems, including real-time architecture and distributed real-time systems; presents a focus on the real-time operating system, covering the concepts of task, memory, and input/output management; provides a detailed step-by-step construction of a real-time operating system kernel, which is then used to test various higher level implementations; describes periodic and aperiodic scheduling, resource management, and distributed scheduling; reviews the process of application design from high-level design methods to low-level details of design and implementation; surveys real-time programming languages and fault tolerance techniques; includes end-of-chapter review questions, extensive C code, numerous examples, and a case study implementing the methods in real-world applications; supplies additional material at an associated website.Requiring only a basic background in computer architecture and operating systems, this practically-oriented work is an invaluable study aid for senior undergraduate and graduate-level students of electrical and computer engineering, and computer science. The text will also serve as a useful general reference for researchers interested in real-time systems.
Distributed Sensing and Intelligent Systems: Proceedings of ICDSIS 2020 (Studies in Distributed Intelligence)
by Mohamed Elhoseny Xiaohui Yuan Salah-Ddine KritThis book is the proceeding of the 1st International Conference on Distributed Sensing and Intelligent Systems (ICDSIS2020) which will be held in The National School of Applied Sciences of Agadir, Ibn Zohr University, Agadir, Morocco on February 01-03, 2020. ICDSIS2020 is co-organized by Computer Vision and Intelligent Systems Lab, University of North Texas, USA as a scientific collaboration event with The National School of Applied Sciences of Agadir, Ibn Zohr University. ICDSIS2020 aims to foster students, researchers, academicians and industry persons in the field of Computer and Information Science, Intelligent Systems, and Electronics and Communication Engineering in general. The volume collects contributions from leading experts around the globe with the latest insights on emerging topics, and includes reviews, surveys, and research chapters covering all aspects of distributed sensing and intelligent systems. The volume is divided into 5 key sections: Distributed Sensing Applications; Intelligent Systems; Advanced theories and algorithms in machine learning and data mining; Artificial intelligence and optimization, and application to Internet of Things (IoT); and Cybersecurity and Secure Distributed Systems. This conference proceeding is an academic book which can be read by students, analysts, policymakers, and regulators interested in Distributed Sensing, Smart Network approaches, Smart Cities, IoT Applications, and Intelligent Applications. It is written in plain and easy language, and describes new concepts when they appear first so that a reader without prior background of the field finds it readable. The book is primarily intended for research students in sensor networks and IoT applications (including intelligent information systems, and smart sensors applications), academics in higher education institutions including universities and vocational colleges, policy makers and legislators.
Distributed Sensor Networks: Image and Sensor Signal Processing (Volume One) (Chapman And Hall/crc Computer And Information Science Ser. #26)
by S. Sitharama Iyengar Richard R. BrooksThe best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for
Distributed Sensor Networks: Sensor Networking and Applications (Volume Two) (Chapman & Hall/CRC Computer and Information Science Series)
by S. Sitharama Iyengar Richard R. BrooksThe best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for
Distributed Serverless Architectures on AWS: Design and Implement Serverless Architectures
by Jithin Jude PaulExplore the serverless world using Amazon Web Services (AWS) and develop various architectures, including those for event-driven and disaster recovery designs. This book will give you an understanding of different distributed serverless architectures and how to build them using AWS components. You will begin with an introduction to serverless components and architectures, before progressing to data platforms and containers. Next, you'll dig deeper into these serverless architectures and how they leverage AWS components through practical use cases. You will also explore designing systems in a multi-cloud paradigm. Author Jithin Jude Paul then demonstrates how efficient serverless architectures are, and the benefits of designing distributed systems globally in a cost-effective way while incorporating a microservices architectural style. Distributed Serverless Architectures with AWS concludes with a discussion of current and future trends in serverless frameworks. After completing this book, you'll be able to design distributed serverless architectures using AWS.What You'll LearnGain an overview of different serverless architectures Design and build distributed systems using serverless componentsBuild serverless data and container platforms on AWSPlan a multi-cloud strategy using serverless components Who This Book Is For Cloud engineers, DevOps engineers, and architects focused on the AWS ecosystem, as well as software engineers/developers working with AWS.
Distributed Simulation
by Okan Topçu Umut Durak Halit Oğuztüzün Levent YilmazThis uniquetext/reference provides a comprehensive review of distributed simulation (DS)from the perspective of Model Driven Engineering (MDE), illustrating how MDEaffects the overall lifecycle of the simulation development process. Numerouspractical case studies are included to demonstrate the utility andapplicability of the methodology, many of which are developed from toolsavailable to download from the public domain. Topics and features: Provides a thorough introduction to the fundamental concepts, principles and processes of modeling and simulation, MDE and high-level architecture Describes a road map for building a DS system in accordance with the MDE perspective, and a technical framework for the development of conceptual models Presents a focus on federate (simulation environment) architectures, detailing a practical approach to the design of federations (i. e. , simulation member design) Discusses the main activities related to scenario management in DS, and explores the process of MDE-based implementation, integration and testing Reviews approaches to simulation evolution and modernization, including architecture-driven modernization for simulation modernization Examines the potential synergies between the agent, DS, and MDE methodologies, suggesting avenues for future research at the intersection of these three fields Distributed Simulation -A Model Driven Engineering Approach is an important resource for allresearchers and practitioners involved in modeling and simulation, and softwareengineering, who may be interested in adopting MDE principles when developingcomplex DS systems.
Distributed Simulation (Computer Science And Engineering Ser.)
by Udo W. Pooch John A. Hamilton David A. NashSimulation is a multi-disciplinary field, and significant simulation research is dispersed across multiple fields of study. Distributed computer systems, software design methods, and new simulation techniques offer synergistic multipliers when joined together in a distributed simulation. Systems of most interest to the simulation practitioner are often the most difficult to model and implement. Distributed Simulation brings together the many complex technologies for distributed simulation. There is strong emphasis on emerging simulation methodologies, including object-oriented, multilevel, and multi-resolution simulation. Finally, one concise text provides a strong foundation for the development of high fidelity simulations in heterogeneous distributed computing environments!
Distributed Space-Time Coding
by Yindi JingDistributed Space-Time Coding (DSTC) is a cooperative relaying scheme that enables high reliability in wireless networks. This brief presents the basic concept of DSTC, its achievable performance, generalizations, code design, and differential use. Recent results on training design and channel estimation for DSTC and the performance of training-based DSTC are also discussed.
Distributed Storage Networks
by Thomas C. JepsenThe worldwide market for SAN and NAS storage is anticipated to grow from US $2 billion in 1999 to over $25 billion by 2004. As business-to-business and business-to-consumer e-commerce matures, even greater demands for management of stored data will arise.With the rapid increase in data storage requirements in the last decade, efficient management of stored data becomes a necessity for the enterprise. A recent UC-Berkeley study predicts that 150,000 terabytes of disk storage will be shipped in 2003. Most financial, insurance, healthcare, and telecommunications institutions are in the process of implementing storage networks that are distributed to some degree. For these institutions, data integrity is critical, and they will spend much time and money on planning.One of the primary obstacles to implementing a storage network cited by enterprise IT managers is a lack of knowledge about storage networking technology and the specific issues involved in extending a Storage Area Network (SAN) or Network Attached Storage (NAS) over the Metropolitan Area Networks (MAN) or Wireless Area Networks (WAN). Distributed Storage Networks : Architecture, Protocols and Management addresses the "terminology gap" between enterprise network planners and telecommunications engineers, who must understand the transport requirements of storage networks in order to implement distributed storage networks. Jepsen comprehensively provides IT managers, planners, and telecommunications professionals with the information they need in order to choose the technologies best suited for their particular environment.* Addresses a hot topic that will become increasingly important in the coming years* Enables high-level managers and planners to make intelligent decisions about network needs.* Includes example network configurations providing solutions to typical user scenarios* Fills the "terminology gap" between enterprise network managers and telecommunications engineers who must understand the transport requirements of storage networks in order to implement distributed storage area networksA fundamental resource for all network managers, planners and network design engineers, as well as telecommunications engineers and engineering, computer science, and information technology students.
Distributed System Design
by Jie WuFuture requirements for computing speed, system reliability, and cost-effectiveness entail the development of alternative computers to replace the traditional von Neumann organization. As computing networks come into being, one of the latest dreams is now possible - distributed computing. Distributed computing brings transparent access to as much computer power and data as the user needs for accomplishing any given task - simultaneously achieving high performance and reliability.The subject of distributed computing is diverse, and many researchers are investigating various issues concerning the structure of hardware and the design of distributed software. Distributed System Design defines a distributed system as one that looks to its users like an ordinary system, but runs on a set of autonomous processing elements (PEs) where each PE has a separate physical memory space and the message transmission delay is not negligible. With close cooperation among these PEs, the system supports an arbitrary number of processes and dynamic extensions.Distributed System Design outlines the main motivations for building a distributed system, including:inherently distributed applicationsperformance/costresource sharingflexibility and extendibilityavailability and fault tolerancescalabilityPresenting basic concepts, problems, and possible solutions, this reference serves graduate students in distributed system design as well as computer professionals analyzing and designing distributed/open/parallel systems.Chapters discuss:the scope of distributed computing systemsgeneral distributed programming languages and a CSP-like distributed control description language (DCDL)expressing parallelism, interprocess communication and synchronization, and fault-tolerant designtwo approaches describing a distributed system: the time-space view and the interleaving viewmutual exclusion and related issues, including election, bidding, and self-stabilizationprevention and detection of deadlockreliability, safety, and security as well as various methods of handling node, communication, Byzantine, and software faultsefficient interprocessor communication mechanisms as well as these mechanisms without specific constraints, such as adaptiveness, deadlock-freedom, and fault-tolerancevirtual channels and virtual networksload distribution problemssynchronization of access to shared data while supporting a high degree of concurrency
Distributed Systems and Applications of Information Filtering and Retrieval
by Alessandro Giuliani Cristian Lai Giovanni SemeraroThis volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the special session on Information Filtering and Retrieval: Novel Distributed Systems and Applications (DART) of the 4th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2012), held in Barcelona, Spain, on 4-7 October 2012. The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as "Multi-Agent Systems", "Natural Language Processing", "Automatic Advertisement", "Customer Interaction Analytics", "Opinion Mining". Contributions have been careful reviewed by experts in the area, who also gave useful suggestions to improve the quality of the volume.
Distributed Systems with Node.js
by Thomas Hunter IIMany companies, from startups to Fortune 500 companies alike, use Node.js to build performant backend services. And engineers love Node.js for its approachable API and familiar syntax. Backed by the world's largest package repository, Node's enterprise foothold is only expected to grow.In this hands-on guide, author Thomas Hunter II proves that Node.js is just as capable as traditional enterprise platforms for building services that are observable, scalable, and resilient. Intermediate to advanced Node.js developers will find themselves integrating application code with a breadth of tooling from each layer of a modern service stack.Learn why running redundant copies of the same Node.js service is necessaryKnow which protocol to choose, depending on the situationFine-tune your application containers for use in productionTrack down errors in a distributed setting to determine which service is at faultSimplify app code and increase performance by offloading work to a reverse proxyBuild dashboards to monitor service health and throughputFind out why so many different tools are required when operating in an enterprise environment