New Advances in Dependability of Networks and Systems e-bog
1313,81 DKK
(inkl. moms 1642,26 DKK)
The book consists of papers on selected topics of dependability analysis in computer systems and networks which were discussed during the 17th DepCoS-RELCOMEX conference held in Wroclaw, Poland, from June 27th to July 1st, 2022. Their collection will be an interesting source material for scientists, researchers, practitioners and students who are dealing with design, analysis and engineeri...
E-bog
1313,81 DKK
Forlag
Springer
Udgivet
26 maj 2022
Genrer
Cybernetics and systems theory
Sprog
English
Format
epub
Beskyttelse
LCP
ISBN
9783031067464
The book consists of papers on selected topics of dependability analysis in computer systems and networks which were discussed during the 17th DepCoS-RELCOMEX conference held in Wroclaw, Poland, from June 27th to July 1st, 2022. Their collection will be an interesting source material for scientists, researchers, practitioners and students who are dealing with design, analysis and engineering of computer systems and networks and must ensure their dependable operation.Being probably the most complex technical systems ever engineered by man (and also, the most dynamically evolving ones), organization of contemporary computer systems and networks cannot be interpreted only as a structure built on the base of unreliable technical resources. Their evaluation must take into account a unique blend of interacting people, networks (together with mobile properties, cloud organization, Internet of Everything, etc.) and a large number of users dispersed geographically and constantly producing an unconceivable number of applications. Research methods being continuously developed for dependability analyses apply newest results of artificial and computational intelligence. Selection of papers in this book illustrates broad range of topics, often multi-disciplinary, which is considered in present-day dependability explorations; it also reveals an increasing role of the latest methods based on machine/deep learning and neural networks in these studies.