Reliable And Intelligent Optimization In Multi Layered Cloud Computing Architectures

One of the major developments in the computing field has been cloud computing, which enables users to do complicated computations that local devices are unable to handle. The computing power and flexibility that have made the cloud so popular do not come without challenges. It is particularly challenging to decide which resources to use, even when they have the same configuration but different levels of performance because of the variable structure of the available resources. Cloud data centers can host millions of virtual machines, and where to locate these machines in the cloud is a difficult problem. Additionally, fulfilling optimization needs is a complex problem. Reliable and Intelligent Optimization in Multi-Layered Cloud Computing Architectures examines ways to meet these challenges. It discusses virtual machine placement techniques and task scheduling techniques that optimize resource utilization and minimize energy consumption of cloud data centers. Placement techniques presented can provide an optimal solution to the optimization problem using multiple objectives. The book focuses on basic design principles and analysis of virtual machine placement techniques and task allocation techniques. It also looks at virtual machine placement techniques that can improve quality-of-service (QoS) in service-oriented architecture (SOA) computing. The aims of virtual machine placement include minimizing energy usage, network traffic, economical cost, maximizing performance, and maximizing resource utilization. Other highlights of the book include: Improving QoS and resource efficiency Fault-tolerant and reliable resource optimization models A reactive fault tolerance method using checkpointing restart Cost and network-aware metaheuristics. Virtual machine scheduling and placement Electricity consumption in cloud data centers Written by leading experts and researchers, this book provides insights and techniques to those dedicated to improving cloud computing and its services.

The book examines virtual machine placement techniques and task scheduling techniques that optimize resource utilization and minimize energy consumption of the cloud data center. The book also focuses on basic design principles and analysis of virtual machine placement techniques and tasks allocation techniques.

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.

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.

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities.

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 serves as a pivotal guide for professionals and researchers within the industrial computing domain, offering an extensive examination of edge computing in industrial environments. Tailored for individuals possessing a foundational understanding of industrial computing systems, it aims to augment their knowledge concerning the role and capabilities of edge computing in this dynamically evolving sector. In an era where real-time, reliable, and scalable computing solutions are of paramount importance, traditional cloud computing models grapple with challenges such as latency, bandwidth limitations, data sovereignty, and privacy concerns. This book positions edge computing as a crucial evolution in industrial data processing and analytics, specifically addressing these challenges. It introduces a distinctive three-layer industrial edge computing architecture that integrates device, edge, and application layers, explicitly designed to accommodate the intricacies of the Industrial Internet of Things (IIoT). Beyond elucidating the theoretical foundations of edge computing, the book delves into its practical applications, with a particular emphasize on edge-assisted model inference as a key scenario. It offers insightful case studies and discussions on the integration of edge computing with artificial intelligence (AI), illustrating how this collaboration is revolutionizing industrial systems. A comprehensive understanding of the material is facilitated by a background in computer science, industrial engineering, IoT, and cloud computing.