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Replication is a topic of interest in the distributed computing, distributed systems, and database communities. Although these communities have traditionally looked at replication from different viewpoints and with different goals (e.g., performance versus fault tolerance), recent developments have led to a convergence of these different goals. The objective of this state-of-the-art survey is not to speculate about the future of replication, but rather to understand the present, to make an assessment of approximately 30 years of research on replication, and to present a comprehensive view of the achievements made during this period of time. This book is the outcome of the seminar entitled A 30-Year Perspective on Replication, which was held at Monte Verità, Ascona, Switzerland, in November 2007. The book is organized in 13 self-contained chapters written by most of the people who have contributed to developing state-of-the-art replication techniques. It presents a comprehensive view of existing solutions, from a theoretical as well as from a practical point of view. It covers replication of processes/objects and of databases; replication for fault tolerance and replication for performance - benign faults and malicious (Byzantine) faults - thus forming a basis for both professionals and students of distributed computing, distributed systems, and databases.
Containing over 300 entries in an A-Z format, the Encyclopedia of Parallel Computing provides easy, intuitive access to relevant information for professionals and researchers seeking access to any aspect within the broad field of parallel computing. Topics for this comprehensive reference were selected, written, and peer-reviewed by an international pool of distinguished researchers in the field. The Encyclopedia is broad in scope, covering machine organization, programming languages, algorithms, and applications. Within each area, concepts, designs, and specific implementations are presented. The highly-structured essays in this work comprise synonyms, a definition and discussion of the topic, bibliographies, and links to related literature. Extensive cross-references to other entries within the Encyclopedia support efficient, user-friendly searchers for immediate access to useful information. Key concepts presented in the Encyclopedia of Parallel Computing include; laws and metrics; specific numerical and non-numerical algorithms; asynchronous algorithms; libraries of subroutines; benchmark suites; applications; sequential consistency and cache coherency; machine classes such as clusters, shared-memory multiprocessors, special-purpose machines and dataflow machines; specific machines such as Cray supercomputers, IBM’s cell processor and Intel’s multicore machines; race detection and auto parallelization; parallel programming languages, synchronization primitives, collective operations, message passing libraries, checkpointing, and operating systems. Topics covered: Speedup, Efficiency, Isoefficiency, Redundancy, Amdahls law, Computer Architecture Concepts, Parallel Machine Designs, Benmarks, Parallel Programming concepts & design, Algorithms, Parallel applications. This authoritative reference will be published in two formats: print and online. The online edition features hyperlinks to cross-references and to additional significant research. Related Subjects: supercomputing, high-performance computing, distributed computing
If the Internet is seen as a single, vast, programmable machine, what is the proper programming paradigm to facilitate development of the new applications it must offer? This state-of-the-art survey deals with this question. The situation we face is similar to that in the 1960s, when a new hardware/software architecture was introduced and it took some time for the programming-language and operating-system specialists to come up with the proper programming paradigms. Now we have the new and exciting paradigm of mobile computing, where computations are not bound to single locations but may move around at will to best use the available computer network resources. This paradigm will have a profound impact on the way distributed applications, in particular Internet applications, are designed and implemented.
It is a pleasure to present the proceedings of the 22nd European Conference on Object-Oriented Programming (ECOOP 2008) held in Paphos, Cyprus. The conference continues to serve a broad object-oriented community with a tech- cal program spanning theory and practice and a healthy mix of industrial and academic participants. This year a strong workshop and tutorial program c- plementedthemaintechnicaltrack.Wehad13workshopsand8tutorials,aswell as the co-located Dynamic Language Symposium (DLS). Finally, the program was rounded out with a keynote by Rachid Guerraoui and a banquet speech by James Noble. As in previous years, two Dahl-Nygaard awards were selected by AITO, and for the ?rst time, the ECOOP Program Committee gave a best paper award. Theproceedingsinclude27papersselectedfrom138submissions.Thepapers werereviewed in a single-blind process with three to ?ve reviews per paper. P- liminaryversionsofthereviewsweremadeavailabletotheauthorsaweekbefore the PC meeting to allow for short (500 words or less) author responses. The - sponses were discussed at the PC meeting and were instrumental in reaching decisions. The PC discussions followed Oscar Nierstrasz’Champion pattern. PC papers had ?ve reviews and were held at a higher standard.
The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.
This open access book constitutes the proceedings of the 27th European Symposium on Programming, ESOP 2018, which took place in Thessaloniki, Greece in April 2018, held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018. The 36 papers presented in this volume were carefully reviewed and selected from 114 submissions. The papers are organized in topical sections named: language design; probabilistic programming; types and effects; concurrency; security; program verification; program analysis and automated verification; session types and concurrency; concurrency and distribution; and compiler verification.
Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators; data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.
This book is devoted to the most difficult part of concurrent programming, namely synchronization concepts, techniques and principles when the cooperating entities are asynchronous, communicate through a shared memory, and may experience failures. Synchronization is no longer a set of tricks but, due to research results in recent decades, it relies today on sane scientific foundations as explained in this book. In this book the author explains synchronization and the implementation of concurrent objects, presenting in a uniform and comprehensive way the major theoretical and practical results of the past 30 years. Among the key features of the book are a new look at lock-based synchronization (mutual exclusion, semaphores, monitors, path expressions); an introduction to the atomicity consistency criterion and its properties and a specific chapter on transactional memory; an introduction to mutex-freedom and associated progress conditions such as obstruction-freedom and wait-freedom; a presentation of Lamport's hierarchy of safe, regular and atomic registers and associated wait-free constructions; a description of numerous wait-free constructions of concurrent objects (queues, stacks, weak counters, snapshot objects, renaming objects, etc.); a presentation of the computability power of concurrent objects including the notions of universal construction, consensus number and the associated Herlihy's hierarchy; and a survey of failure detector-based constructions of consensus objects. The book is suitable for advanced undergraduate students and graduate students in computer science or computer engineering, graduate students in mathematics interested in the foundations of process synchronization, and practitioners and engineers who need to produce correct concurrent software. The reader should have a basic knowledge of algorithms and operating systems.