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.

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

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.

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.