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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.
An introduction to fundamental theories of concurrent computation and associated programming languages for developing distributed and mobile computing systems. Starting from the premise that understanding the foundations of concurrent programming is key to developing distributed computing systems, this book first presents the fundamental theories of concurrent computing and then introduces the programming languages that help develop distributed computing systems at a high level of abstraction. The major theories of concurrent computation—including the π-calculus, the actor model, the join calculus, and mobile ambients—are explained with a focus on how they help design and reason about distributed and mobile computing systems. The book then presents programming languages that follow the theoretical models already described, including Pict, SALSA, and JoCaml. The parallel structure of the chapters in both part one (theory) and part two (practice) enable the reader not only to compare the different theories but also to see clearly how a programming language supports a theoretical model. The book is unique in bridging the gap between the theory and the practice of programming distributed computing systems. It can be used as a textbook for graduate and advanced undergraduate students in computer science or as a reference for researchers in the area of programming technology for distributed computing. By presenting theory first, the book allows readers to focus on the essential components of concurrency, distribution, and mobility without getting bogged down in syntactic details of specific programming languages. Once the theory is understood, the practical part of implementing a system in an actual programming language becomes much easier.
Mit der Verfügbarkeit verteilter Systeme wächst der Bedarf an einer fundamentalen Diskussion dieses Gebiets. Hier ist sie! Abgedeckt werden die grundlegenden Konzepte wie Zeit, Zustand, Gleichzeitigkeit, Reihenfolge, Kenntnis, Fehler und Übereinstimmung. Die Betonung liegt auf der Entwicklung allgemeiner Mechanismen, die auf eine Vielzahl von Problemen angewendet werden können. Sorgfältig ausgewählte Beispiele (Taktgeber, Sperren, Kameras, Sensoren, Controller, Slicer und Syncronizer) dienen gleichzeitig der Vertiefung theoretischer Aspekte und deren Umsetzung in die Praxis. Alle vorgestellten Algorithmen werden mit durchschaubaren, induktionsbasierten Verfahren bewiesen.
A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.
Presents the locality-sensitive approach to distributed network algorithms-the utilization of locality to simplify control structures and algorithms and reduce their costs. The author begins with an introductory exposition of distributed network algorithms focusing on topics that illustrate the role of locality in distributed algorithmic techniques. He then introduces locality-preserving network representations and describes sequential and distributed techniques for their construction. Finally, the applicability of the locality-sensitive approach is demonstrated through several applications. Gives a thorough exposition of network spanners and other locality-preserving network representations such as sparse covers and partitions. The book is useful for computer scientists interested in distributed computing, electrical engineers interested in network architectures and protocols, and for discrete mathematicians and graph theorists.
* Comprehensive introduction to the fundamental results in the mathematical foundations of distributed computing * Accompanied by supporting material, such as lecture notes and solutions for selected exercises * Each chapter ends with bibliographical notes and a set of exercises * Covers the fundamental models, issues and techniques, and features some of the more advanced topics
Distributed Computing Through Combinatorial Topology describes techniques for analyzing distributed algorithms based on award winning combinatorial topology research. The authors present a solid theoretical foundation relevant to many real systems reliant on parallelism with unpredictable delays, such as multicore microprocessors, wireless networks, distributed systems, and Internet protocols. Today, a new student or researcher must assemble a collection of scattered conference publications, which are typically terse and commonly use different notations and terminologies. This book provides a self-contained explanation of the mathematics to readers with computer science backgrounds, as well as explaining computer science concepts to readers with backgrounds in applied mathematics. The first section presents mathematical notions and models, including message passing and shared-memory systems, failures, and timing models. The next section presents core concepts in two chapters each: first, proving a simple result that lends itself to examples and pictures that will build up readers' intuition; then generalizing the concept to prove a more sophisticated result. The overall result weaves together and develops the basic concepts of the field, presenting them in a gradual and intuitively appealing way. The book's final section discusses advanced topics typically found in a graduate-level course for those who wish to explore further. - Named a 2013 Notable Computer Book for Computing Methodologies by Computing Reviews - Gathers knowledge otherwise spread across research and conference papers using consistent notations and a standard approach to facilitate understanding - Presents unique insights applicable to multiple computing fields, including multicore microprocessors, wireless networks, distributed systems, and Internet protocols - Synthesizes and distills material into a simple, unified presentation with examples, illustrations, and exercises
This book documents the main results developed in the course of the European project "Basic Research on Advanced Distributed Computing: From Algorithms to Systems (BROADCAST)". Eight major European research groups in distributed computing cooporated on this projects, from 1992 to 1999. The 21 thoroughly cross-reviewed final full papers present the state-of-the art results on distributed systems in a coherent way. The book is divided in parts on distributed algorithms, systems architecture, applications support, and case studies.
This book describes the key concepts, principles and implementation options for creating high-assurance cloud computing solutions. The guide starts with a broad technical overview and basic introduction to cloud computing, looking at the overall architecture of the cloud, client systems, the modern Internet and cloud computing data centers. It then delves into the core challenges of showing how reliability and fault-tolerance can be abstracted, how the resulting questions can be solved, and how the solutions can be leveraged to create a wide range of practical cloud applications. The author’s style is practical, and the guide should be readily understandable without any special background. Concrete examples are often drawn from real-world settings to illustrate key insights. Appendices show how the most important reliability models can be formalized, describe the API of the Isis2 platform, and offer more than 80 problems at varying levels of difficulty.
Harness the power of multiple computers using Python through this fast-paced informative guide About This Book You'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant Make use of Amazon Web Services along with Python to establish a powerful remote computation system Train Python to handle data-intensive and resource hungry applications Who This Book Is For This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks. What You Will Learn Get an introduction to parallel and distributed computing See synchronous and asynchronous programming Explore parallelism in Python Distributed application with Celery Python in the Cloud Python on an HPC cluster Test and debug distributed applications In Detail CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more. Style and Approach This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.