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The concept of autonomic computing seeks to reduce the complexity
of pervasively ubiquitous system management and maintenance by
shifting the responsibility for low-level tasks from humans to the
system while allowing humans to concentrate on high-level tasks.
This is achieved by building self-managing systems that are
generally capable of self-configuring, self-healing,
self-optimising, and self-protecting. Trustworthy autonomic
computing technologies are being applied in datacentre and cloud
management, smart cities and autonomous systems including
driverless cars. However, there are still significant challenges to
achieving trustworthiness. This book covers challenges and
solutions in autonomic computing trustworthiness from methods and
techniques to achieve consistent and reliable system
self-management. Researchers, developers and users need to be
confident that an autonomic self-managing system will remain
correct in the face of any possible contexts and environmental
inputs. The book is aimed at researchers in autonomic computing,
autonomics and trustworthy autonomics. This will be a go-to book
for foundational knowledge, proof of concepts and novel trustworthy
autonomic techniques and approaches. It will be useful to lecturers
and students of autonomic computing, autonomics and multi-agent
systems who need an easy-to-use text with sample codes, exercises,
use-case demonstrations. This is also an ideal tutorial guide for
independent study with simple and well documented diagrams to
explain techniques and processes.
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