Mastering Distributed Algorithms
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Mastering Distributed Algorithms
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Author : Roger Wattenhofer
language : en
Publisher:
Release Date : 2020-03-23
Mastering Distributed Algorithms written by Roger Wattenhofer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-23 with categories.
About the book: The Internet is a distributed system, but so are wireless communication, cloud or parallel computing, multi-core systems, mobile networks. Also an ant colony, a brain, or even the human society can be modeled as distributed systems. In this book we will be highlighting common themes and techniques. In particular, we study some of the fundamental issues underlying the design of distributed systems, for example, communication, coordination, fault-tolerance, locality, parallelism, symmetry breaking, synchronization, and uncertainty.About the author: Roger Wattenhofer is a professor at ETH Zurich. Before joining ETH Zurich, he was at Brown University and Microsoft Research. His research interests include fault-tolerant distributed systems, efficient network algorithms, and cryptocurrencies such as Bitcoin. He has published more than 300 scientific articles. In 2017, he published the book Blockchain Science.
Distributed Algorithms For Message Passing Systems
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Author : Michel Raynal
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Distributed Algorithms For Message Passing Systems written by Michel Raynal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Computers categories.
Distributed computing is at the heart of many applications. It arises as soon as one has to solve a problem in terms of entities -- such as processes, peers, processors, nodes, or agents -- that individually have only a partial knowledge of the many input parameters associated with the problem. In particular each entity cooperating towards the common goal cannot have an instantaneous knowledge of the current state of the other entities. Whereas parallel computing is mainly concerned with 'efficiency', and real-time computing is mainly concerned with 'on-time computing', distributed computing is mainly concerned with 'mastering uncertainty' created by issues such as the multiplicity of control flows, asynchronous communication, unstable behaviors, mobility, and dynamicity. While some distributed algorithms consist of a few lines only, their behavior can be difficult to understand and their properties hard to state and prove. The aim of this book is to present in a comprehensive way the basic notions, concepts, and algorithms of distributed computing when the distributed entities cooperate by sending and receiving messages on top of an asynchronous network. The book is composed of seventeen chapters structured into six parts: distributed graph algorithms, in particular what makes them different from sequential or parallel algorithms; logical time and global states, the core of the book; mutual exclusion and resource allocation; high-level communication abstractions; distributed detection of properties; and distributed shared memory. The author establishes clear objectives per chapter and the content is supported throughout with illustrative examples, summaries, exercises, and annotated bibliographies. This book constitutes an introduction to distributed computing and is suitable for advanced undergraduate students or graduate students in computer science and computer engineering, graduate students in mathematics interested in distributed computing, and practitioners and engineers involved in the design and implementation of distributed applications. The reader should have a basic knowledge of algorithms and operating systems.
Distributed Algorithms
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Author : Wan Fokkink
language : en
Publisher: MIT Press
Release Date : 2013-12-06
Distributed Algorithms written by Wan Fokkink and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-06 with Computers categories.
A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.
Mastering Distributed Computing
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Distributed Computing written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Uncover the Art of Seamless Distributed Computing with "Mastering Distributed Computing" In the dynamic realm of modern computing, the ability to harness the power of distributed systems is paramount. "Mastering Distributed Computing" is your definitive guide to mastering the art of seamlessly orchestrating distributed resources for optimal performance and scalability. Whether you're an experienced software engineer or a newcomer to the world of distributed computing, this book equips you with the knowledge and skills needed to navigate the intricacies of distributed systems. About the Book: "Mastering Distributed Computing" takes you on an enlightening journey through the intricacies of distributed computing, from foundational concepts to advanced techniques. From distributed architectures to consensus algorithms, this book covers it all. Each chapter is meticulously designed to provide both a deep understanding of the concepts and practical applications in real-world scenarios. Key Features: · Foundational Principles: Build a strong foundation by understanding the core principles of distributed systems, including scalability, fault tolerance, and data consistency. · Distributed Architectures: Explore a range of distributed architectures, including client-server, peer-to-peer, microservices, and serverless, understanding their strengths and applications. · Consistency and Replication: Dive into the complexities of data consistency and replication strategies, including eventual consistency, strong consistency, and distributed databases. · Distributed Algorithms: Master fundamental distributed algorithms, such as leader election, distributed locking, and distributed transactions, for coordinating actions across nodes. · Scaling Strategies: Discover strategies for scaling distributed systems horizontally and vertically, ensuring optimal performance as workloads grow. · Fault Tolerance: Understand mechanisms for building fault-tolerant systems, including redundancy, replication, and failure detection and recovery. · Real-World Use Cases: Gain insights from real-world examples spanning industries, from finance and e-commerce to social media and beyond. · Cloud and Edge Computing: Explore the role of distributed computing in cloud environments and edge computing scenarios, and their impact on modern applications. · Security and Privacy: Explore best practices for securing distributed systems, data encryption, access control, and compliance. Who This Book Is For: "Mastering Distributed Computing" is designed for software engineers, architects, developers, and anyone passionate about effective distributed system design. Whether you're seeking to enhance your skills or embark on a journey toward becoming a distributed computing expert, this book provides the insights and tools to navigate the complexities of distributed systems. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Mastering Algorithms With C
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Author : Kyle Loudon
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 1999
Mastering Algorithms With C written by Kyle Loudon and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
Implementations, as well as interesting, real-world examples of each data structure and algorithm, are shown in the text. Full source code appears on the accompanying disk.
Distributed Computing
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Author : Ajay D. Kshemkalyani
language : en
Publisher: Cambridge University Press
Release Date : 2011-03-03
Distributed Computing written by Ajay D. Kshemkalyani and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-03 with Technology & Engineering categories.
Designing distributed computing systems is a complex process requiring a solid understanding of the design problems and the theoretical and practical aspects of their solutions. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource. Additional resources are available online at www.cambridge.org/9780521876346.
Distributed Algorithms
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Author : Fourré Sigs
language : en
Publisher: Independently Published
Release Date : 2019-01-31
Distributed Algorithms written by Fourré Sigs and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with categories.
AN ELABORATE YET BEGINNER-FRIENDLY GUIDE TO DISTRIBUTED ALGORITHMS Distributed Algorithms, a non-trivial and highly evolving field of active research, is often presented in most publications using a heavy accompaniment of mathematical techniques and notations. Aimed squarely at beginners as well as experienced practitioners, this book attempts to demystify and explicate the subject of distributed algorithms using a highly expansive and verbose style of treatment. Covering scores of landmark algorithms in the field of distributed computing, the approach is to present and analyse each topic using a minimum of mathematical exposition, reverting instead to a fluid style of description in plain English. A mathematical presentation is avoided altogether whenever such a move does not reduce the quality of the analysis at hand. Elsewhere, the effort always is to talk and guide the reader through the relevant math without resorting to a series of equations. To backup such a style of treatment, each topic is accompanied by a multitude of examples, flowcharts, and diagrams. The book is divided into three parts; the first part deals with fundamentals, the second and largest of the three is all about algorithms specific to message passing networks, while the last one focuses on shared memory algorithms. The beginning of the book dedicates a few chapters to the basics - including a quick orientation on the underlying platform, i.e. distributed systems, their characteristics, advantages, challenges, and so on. Some of the earlier chapters also address basic algorithms and techniques relevant to distributed computing environments before moving on to progressively complex algorithms and results - en route to the later chapters in the second part which deal with widely used 'industrial-strength' protocols such as Paxos and Raft. The third part of the book does assume a basic orientation towards computer programming, and presents numerous shared memory algorithms where each one is accompanied by a detailed description, analysis, pseudo code, and in some cases, code (C or C++). Whenever actual code is used, the syntax is kept as basic as possible - incorporating only elementary features of the language - so that newbie programmers can follow the presentation smoothly. Lastly, the target audience of the book is wide enough to cover beginners such as students or graduates joining the industry, experienced professionals wishing to migrate from monolithic frameworks to distributed ones, as well as readers with years of experience on the subject of distributed computing. The style of presentation is selected with the first two classes of readers in mind: those who wish to quickly ramp up on the subject of distributed algorithms for professional reasons or personal ones. While staying true to the stated aim, the book does not shy away from dealing with complex topics. A concise list of content information follows: Introduction to distributed systems Properties of distributed data stores and Brewer's theorem Building blocks: unicast, broadcast, algorithms in cubes Leader election algorithms: for ring/generic networks Consensus algorithms: synchronous/asynchronous variants for message passing and shared memory systems Distributed commits, Paxos, Raft Graph algorithms Routing algorithms Time and order Mutual exclusion: for message passing networks Debug algorithms: snapshot, deadlock/termination detection Shared memory: practical problems, mutual exclusion, consensus, resource allocation About the author Fourré Sigs is an industry veteran with over 25 years of experience in systems programming, networking, and highly scalable and secure distributed service architectures.
Mastering Blockchain
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Author : Imran Bashir
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-30
Mastering Blockchain written by Imran Bashir and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Computers categories.
Learn about cryptography and cryptocurrencies, so you can build highly secure, decentralized applications and conduct trusted in-app transactions. Key Features Get to grips with the underlying technical principles and implementations of blockchain Build powerful applications using Ethereum to secure transactions and create smart contracts Explore cryptography, mine cryptocurrencies, and solve scalability issues with this comprehensive guide Book Description A blockchain is a distributed ledger that is replicated across multiple nodes and enables immutable, transparent and cryptographically secure record-keeping of transactions. The blockchain technology is the backbone of cryptocurrencies, and it has applications in finance, government, media and almost all other industries. Mastering Blockchain, Second Edition has been thoroughly updated and revised to provide a detailed description of this leading technology and its implementation in the real world. This book begins with the technical foundations of blockchain technology, teaching you the fundamentals of distributed systems, cryptography and how it keeps data secure. You will learn about the mechanisms behind cryptocurrencies and how to develop applications using Ethereum, a decentralized virtual machine. You will also explore different other blockchain solutions and get an introduction to business blockchain frameworks under Hyperledger, a collaborative effort for the advancement of blockchain technologies hosted by the Linux Foundation. You will also be shown how to implement blockchain solutions beyond currencies, Internet of Things with blockchain, blockchain scalability, and the future scope of this fascinating and powerful technology. What you will learn Master the theoretical and technical foundations of the blockchain technology Understand the concept of decentralization, its impact, and its relationship with blockchain technology Master how cryptography is used to secure data - with practical examples Grasp the inner workings of blockchain and the mechanisms behind bitcoin and alternative cryptocurrencies Understand the theoretical foundations of smart contracts Learn how Ethereum blockchain works and how to develop decentralized applications using Solidity and relevant development frameworks Identify and examine applications of the blockchain technology - beyond currencies Investigate alternative blockchain solutions including Hyperledger, Corda, and many more Explore research topics and the future scope of blockchain technology Who this book is for This book will appeal to those who wish to build fast, highly secure, transactional applications. It targets people who are familiar with the concept of blockchain and are comfortable with a programming language.
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-25
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-25 with Computers categories.
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.