Sukumar Ghosh

Distributed Systems: An Algorithmic Approach, Second Edition provides a balanced and straightforward treatment of the underlying theory and practical applications of distributed computing. As in the previous version, the language is kept as unobscured as possible—clarity is given priority over mathematical formalism. This easily digestible text:Features significant updates that mirror the phenomenal growth of distributed systemsExplores new topics related to peer-to-peer and social networksIncludes fresh exercises, examples, and case studiesSupplying a solid understanding of the key principles of distributed computing and their relationship to real-world applications, Distributed Systems: An Algorithmic Approach, Second Edition makes both an ideal textbook and a handy professional reference.

Distributed Systems: An Algorithmic Approach, Second Edition provides a balanced and straightforward treatment of the underlying theory and practical applications of distributed computing. As in the previous version, the language is kept as unobscured as possible—clarity is given priority over mathematical formalism. This easily digestible text:Features significant updates that mirror the phenomenal growth of distributed systemsExplores new topics related to peer-to-peer and social networksIncludes fresh exercises, examples, and case studiesSupplying a solid understanding of the key principles of distributed computing and their relationship to real-world applications, Distributed Systems: An Algorithmic Approach, Second Edition makes both an ideal textbook and a handy professional reference.

Self- governing control is a defining characteristic of autonomous computing machinery. Autonomy implies some degree of independence, and when a system's ability to achieve its mission is independent of how it is initialized, the system is self-stabilizing. Application of self-stabilization to system and network components is motivated by core concerns of fault-tolerance in distributed systems. Self-stabilization is a solution to problems of transient memory faults and systems with dynamic reconfigurations. Research in self-stabilization explores many of the classic themes of distributed computing (distributed graph algorithms, mutual exclusion, distributed agreement). Recent papers combine self-stabilization with traditional forms of fault-tolerance, consider methodological issues for the design of self-stabilizing systems, investigate randomized techniques, and apply stabilization to new networking models. The workshop brings together concerns from theory and practice of self-stabilization.