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Greetings and welcome to ApPLIED 2022 on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems. The ApPLIED workshop aims to bring designers and practitioners of distributed systems from both academia and industry together to share their points of view and experiences in designing and building distributed systems. Our goal is to make ApPLIED the premier forum for stimulating discussions in the intersection of the theory and the practice of distributed systems. We have attempted to keep its scope broad to encourage participation from both academia and industry. We are honored and delighted to have Ken Birman (Cornell, USA) and Roger Wattenhofer (ETH Zurich, Switzerland) as keynote speakers talking about 'Cascade: An Edge Computing Platform for Real-time Machine Intelligence', and respectively, 'Graph Neural Networks as Application of Distributed Algorithms'. The ApPLIED workshop has an exciting technical program with five invited papers, four accepted regular submissions, and one short research statement. Six regular papers and one short research statement were submitted. The acceptance rate of regular submissions was 66%. Every submission was reviewed by at least three reviewers. In the proceedings, the five invited papers appear before the five peer-reviewed papers. The short research statement appears last. ApPLIED 2022 is co-located with PODC 2022. Earlier editions of ApPLIED were collocated with PODC 2018, DISC 2019, and PODC 2021. More information can be found at tinyurl.com/ApPLIED2022.
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Resource Allocation --- mutual exclusion --- Parallelism --- synchronization --- Distributed Algorithms
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Computer science. --- Compilers --- Distributed algorithms --- External interfaces for robotics
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Computer security --- Microgrids (Smart power grids) --- Distributed algorithms.
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Computer security --- Microgrids (Smart power grids) --- Distributed algorithms.
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Programming --- Parallel programming (Computer science) --- Programmation parallèle (Informatique) --- 681.3*D1 --- Computer programming --- Parallel processing (Electronic computers) --- Programming techniques--See also {681.3*E} --- 681.3*D1 Programming techniques--See also {681.3*E} --- Programmation parallèle (Informatique) --- Concurrent Programming --- Distributed Algorithms --- Distributed
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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 --- Electronic data processing --- Distributed processing --- Mathematics --- Computer science. --- Computer hardware. --- Computer communication systems. --- Computer programming. --- Computers. --- Computer Science. --- Theory of Computation. --- Computer Communication Networks. --- Programming Techniques. --- Computer Hardware. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Informatics --- Science --- Programming --- Information theory. --- Communication theory --- Communication --- Distributed algorithms. --- Mathematics. --- Electronic data processing - Distributed processing - Mathematics
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This 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.
Control engineering. --- Mathematical optimization. --- Computers. --- Control and Systems Theory. --- Optimization. --- Information Systems and Communication Service. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Distributed algorithms. --- Parallel algorithms
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