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This book discusses various aspects of cloud computing, in which
trust and fault-tolerance models are included in a multilayered,
cloud architecture. The authors present a variety of trust and
fault models used in the cloud, comparing them based on their
functionality and the layer in the cloud to which they respond.
Various methods are discussed that can improve the performance of
cloud architectures, in terms of trust and fault-tolerance, while
providing better performance and quality of service to user. The
discussion also includes new algorithms that overcome drawbacks of
existing methods, using a performance matrix for each
functionality. This book provide readers with an overview of cloud
computing and how trust and faults in cloud datacenters affects the
performance and quality of service assured to the users. Discusses
fundamental issues related to trust and fault-tolerance in Cloud
Computing; Describes trust and fault management techniques in multi
layered cloud architecture to improve security, reliability and
performance of the system; Includes methods to enhance power
efficiency and network efficiency, using trust and fault based
resource allocation.
Unique selling point: Combines theory with practice and
applications for advanced intelligent healthcare informatics Core
audience: Researchers and academics in healthcare informatics and
machine learning Place in the market: Reference work
A new era of complexity science is emerging, in which nature- and
bio-inspired principles are being applied to provide solutions. At
the same time, the complexity of systems is increasing due to such
models like the Internet of Things (IoT) and fog computing. Will
complexity science, applying the principles of nature, be able to
tackle the challenges posed by highly complex networked systems?
Bio-Inspired Optimization in Fog and Edge Computing: Principles,
Algorithms, and Systems is an attempt to answer this question. It
presents innovative, bio-inspired solutions for fog and edge
computing and highlights the role of machine learning and
informatics. Nature- or biological-inspired techniques are
successful tools to understand and analyze a collective behavior.
As this book demonstrates, algorithms, and mechanisms of
self-organization of complex natural systems have been used to
solve optimization problems, particularly in complex systems that
are adaptive, ever-evolving, and distributed in nature. The
chapters look at ways of enhancingto enhance the performance of fog
networks in real-world applications using nature-based optimization
techniques. They discuss challenges and provide solutions to the
concerns of security, privacy, and power consumption in cloud data
center nodes and fog computing networks. The book also examines
how: The existing fog and edge architecture is used to provide
solutions to future challenges. A geographical information system
(GIS) can be used with fog computing to help users in an urban
region access prime healthcare. An optimization framework helps in
cloud resource management. Fog computing can improve the quality,
quantity, long-term viability, and cost-effectiveness in
agricultural production. Virtualization can support fog computing,
increase resources to be allocated, and be applied to different
network layers. The combination of fog computing and IoT or cloud
computing can help healthcare workers predict and analyze diseases
in patients.
Machine Learning and Models for Optimization in Cloud's main aim is
to meet the user requirement with high quality of service, least
time for computation and high reliability. With increase in
services migrating over cloud providers, the load over the cloud
increases resulting in fault and various security failure in the
system results in decreasing reliability. To fulfill this
requirement cloud system uses intelligent metaheuristic and
prediction algorithm to provide resources to the user in an
efficient manner to manage the performance of the system and plan
for upcoming requests. Intelligent algorithm helps the system to
predict and find a suitable resource for a cloud environment in
real time with least computational complexity taking into mind the
system performance in under loaded and over loaded condition. This
book discusses the future improvements and possible intelligent
optimization models using artificial intelligence, deep learning
techniques and other hybrid models to improve the performance of
cloud. Various methods to enhance the directivity of cloud services
have been presented which would enable cloud to provide better
services, performance and quality of service to user. It talks
about the next generation intelligent optimization and fault model
to improve security and reliability of cloud. Key Features *
Comprehensive introduction to cloud architecture and its service
models. * Vulnerability and issues in cloud SAAS, PAAS and IAAS *
Fundamental issues related to optimizing the performance in Cloud
Computing using meta-heuristic, AI and ML models * Detailed study
of optimization techniques, and fault management techniques in
multi layered cloud. * Methods to improve reliability and fault in
cloud using nature inspired algorithms and artificial neural
network. * Advanced study of algorithms using artificial
intelligence for optimization in cloud * Method for power efficient
virtual machine placement using neural network in cloud * Method
for task scheduling using metaheuristic algorithms. * A study of
machine learning and deep learning inspired resource allocation
algorithm for cloud in fault aware environment. This book aims to
create a research interest & motivation for graduates degree or
post-graduates. It aims to present a study on optimization
algorithms in cloud for researchers to provide them with a glimpse
of future of cloud computing in the era of artificial intelligence.
This book discusses various aspects of cloud computing, in which
trust and fault-tolerance models are included in a multilayered,
cloud architecture. The authors present a variety of trust and
fault models used in the cloud, comparing them based on their
functionality and the layer in the cloud to which they respond.
Various methods are discussed that can improve the performance of
cloud architectures, in terms of trust and fault-tolerance, while
providing better performance and quality of service to user. The
discussion also includes new algorithms that overcome drawbacks of
existing methods, using a performance matrix for each
functionality. This book provide readers with an overview of cloud
computing and how trust and faults in cloud datacenters affects the
performance and quality of service assured to the users. Discusses
fundamental issues related to trust and fault-tolerance in Cloud
Computing; Describes trust and fault management techniques in multi
layered cloud architecture to improve security, reliability and
performance of the system; Includes methods to enhance power
efficiency and network efficiency, using trust and fault based
resource allocation.
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