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Stabilization, Safety, and Security of Distributed Systems - 24th International Symposium, SSS 2022, Clermont-Ferrand, France, November 15-17, 2022, Proceedings (Paperback, 1st ed. 2022)
Stephane Devismes, Franck Petit, Karine Altisen, Giuseppe Antonio Di Luna, Antonio Fernandez Anta
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R2,349
Discovery Miles 23 490
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Ships in 10 - 15 working days
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This book constitutes the proceedings of 24th International
Symposium, SSS 2022, which took place in Clermont-Ferrand, France,
in November 2022.The 17 regular papers together with 4 invited
papers and 7 brief announcements, included in this volume were
carefully reviewed and selected from 58 submissions. The SSS 2022
focus on systems built such that they are able to provide on their
own guarantees on their structure, performance, and/or security in
the face of an adverse environment. The Symposium presents three
tracks reflecting major trends related to the conference: (i)
Self-stabilizing Systems: Theory and Practice, (ii) Concurrent and
Distributed Computing: Foundations, Faulttolerance, and Security,
and (iii) Dynamic, Mobile, and Nature-Inspired Computing.
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Stabilization, Safety, and Security of Distributed Systems - 22nd International Symposium, SSS 2020, Austin, TX, USA, November 18-21, 2020, Proceedings (Paperback, 1st ed. 2020)
Stephane Devismes, Neeraj Mittal
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R1,570
Discovery Miles 15 700
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 22nd
International Symposium on Stabilization, Safety, and Security of
Distributed Systems, SSS 2020, held in Austin, TX, USA, in November
2020.The 16 full papers, 7 short and 2 invited papers presented
were carefully reviewed and selected from 44 submissions. The
papers deal with the design and development of distributed systems
with a focus on systems that are able to provide guarantees on
their structure, performance, and/or security in the face of an
adverse operational environment.
This book aims at being a comprehensive and pedagogical
introduction to the concept of self-stabilization, introduced by
Edsger Wybe Dijkstra in 1973. Self-stabilization characterizes the
ability of a distributed algorithm to converge within finite time
to a configuration from which its behavior is correct (i.e.,
satisfies a given specification), regardless the arbitrary initial
configuration of the system. This arbitrary initial configuration
may be the result of the occurrence of a finite number of transient
faults. Hence, self-stabilization is actually considered as a
versatile non-masking fault tolerance approach, since it recovers
from the effect of any finite number of such faults in an unified
manner. Another major interest of such an automatic recovery method
comes from the difficulty of resetting malfunctioning devices in a
large-scale (and so, geographically spread) distributed system (the
Internet, Pair-to-Pair networks, and Delay Tolerant Networks are
examples of such distributed systems). Furthermore,
self-stabilization is usually recognized as a lightweight property
to achieve fault tolerance as compared to other classical fault
tolerance approaches. Indeed, the overhead, both in terms of time
and space, of state-of-the-art self-stabilizing algorithms is
commonly small. This makes self-stabilization very attractive for
distributed systems equipped of processes with low computational
and memory capabilities, such as wireless sensor networks. After
more than 40 years of existence, self-stabilization is now
sufficiently established as an important field of research in
theoretical distributed computing to justify its teaching in
advanced research-oriented graduate courses. This book is an
initiation course, which consists of the formal definition of
self-stabilization and its related concepts, followed by a deep
review and study of classical (simple) algorithms, commonly used
proof schemes and design patterns, as well as premium results
issued from the self-stabilizing community. As often happens in the
self-stabilizing area, in this book we focus on the proof of
correctness and the analytical complexity of the studied
distributed self-stabilizing algorithms. Finally, we underline that
most of the algorithms studied in this book are actually dedicated
to the high-level atomic-state model, which is the most commonly
used computational model in the self-stabilizing area. However, in
the last chapter, we present general techniques to achieve
self-stabilization in the low-level message passing model, as well
as example algorithms.
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