Cooperative network supercomputing is becoming increasingly popular for harnessing the power of the global Internet computing platform. A typical Internet supercomputer consists of a master computer or server and a large number of computers called workers, performing computation on behalf of the master. Despite the simplicity and benefits of a single master approach, as the scale of such computing environments grows, it becomes unrealistic to assume the existence of the infallible master that is able to coordinate the activities of multitudes of workers. Large-scale distributed systems are inherently dynamic and are subject to perturbations, such as failures of computers and network links, thus it is also necessary to consider fully distributed peer-to-peer solutions. We present a study of cooperative computing with the focus on modeling distributed computing settings, algorithmic techniques enabling one to combine efficiency and fault-tolerance in distributed systems, and the exposition of trade-offs between efficiency and fault-tolerance for robust cooperative computing. The focus of the exposition is on the abstract problem, called Do-All, and formulated in terms of a system of cooperating processors that together need to perform a collection of tasks in the presence of adversity. Our presentation deals with models, algorithmic techniques, and analysis. Our goal is to present the most interesting approaches to algorithm design and analysis leading to many fundamental results in cooperative distributed computing. The algorithms selected for inclusion are among the most efficient that additionally serve as good pedagogical examples. Each chapter concludes with exercises and bibliographic notes that include a wealth of references to related work and relevant advanced results. Table of Contents: Introduction / Distributed Cooperation and Adversity / Paradigms and Techniques / Shared-Memory Algorithms / Message-Passing Algorithms / The Do-All Problem in Other Settings / Bibliography / Authors' Biographies

This book studies algorithmic issues associated with cooperative execution of multiple independent tasks by distributed computing agents including partitionable networks. It provides the most significant algorithmic solution developed and available today for do-all computing for distributed systems (including partitionable networks), and is the first monograph that deals with do-all computing for distributed systems. The book is structured to meet the needs of a professional audience composed of researchers and practitioners in industry. This volume is also suitable for graduate-level students in computer science.

This book presents several significant advances in algorithms designed to solve the Do-All problem in distributed message-passing settings under various models of adversity, including processor crashes, asynchrony, message delays, network partitions, and malicious processor behaviors. Upper and lower bounds are presented, demonstrating the extent to which efficiency can be combined with fault-tolerance. This book contains the recent advances in the principles of efficient and fault-tolerant cooperative computing, narrowing the gap between abstract models of dependable network computing and realistic distributed systems.

This book constitutes the revised selected papers of the 8th International Conference on Networked Systems, NETYS 2020, held in Marrakech, Morocco, in June 2020.*The 18 revised full papers and 4 short papers presented together with 3 invited papers were carefully reviewed and selected from 46 submissions. The papers cover all aspects related to the design and the development of these systems, including, but not restricted to, concurrent and distributed algorithms, parallel/concurrent/distributed programming, multi-core architectures, formal verification, distributed databases, cloud systems, networks, security, formal verification, etc. *The conference was held virtually due to the COVID-19 pandemic.