Keqin Li

This book uses automotive embedded systems as an example to introduce functional safety assurance and safety-aware cost optimization. The book explores functional safety assurance from the perspectives of verification, enhancement, and validation. The functional safety assurance methods implement a safe and efficient assurance system that integrates safety verification, enhancement, and validation. The assurance methods offered in this book could provide a reasonable and scientific theoretical basis for the subsequent formulation of automotive functional safety standards. The safety-aware cost optimization methods divide cost types according to the essential differences of various costs in system design and establish reasonable models based on different costs. The cost optimization methods provided in this book could give appropriate cost optimization solutions for the cost-sensitive automotive industry, thereby achieving effective cost management and control. Functional safety assurance methods and safety-aware cost optimization support each other and jointly build the architecture of functional safety design methodologies for automotive embedded systems. The work aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in hardware cost optimization and development cost optimization design methods based on ensuring functional safety in general.

This book provides an in-depth examination of recent research advances in cloud-edge-end computing, covering theory, technologies, architectures, methods, applications, and future research directions. It aims to present state-of-the-art models and optimization methods for fusing and integrating clouds, edges, and devices. Cloud-edge-end computing provides users with low-latency, high-reliability, and cost-effective services through the fusion and integration of clouds, edges, and devices. As a result, it is now widely used in various application scenarios. The book introduces the background and fundamental concepts of clouds, edges, and devices, and details the evolution, concepts, enabling technologies, architectures, and implementations of cloud-edge-end computing. It also examines different types of cloud-edge-end orchestrated systems and applications and discusses advanced performance modeling approaches, as well as the latest research on offloading and scheduling policies. It also covers resource management methods for optimizing application performance on cloud-edge-end orchestrated systems. The intended readers of this book are researchers, undergraduate and graduate students, and engineers interested in cloud computing, edge computing, and the Internet of Things. The knowledge of this book will enrich our readers to be at the forefront of cloud-edge-end computing.

This book will serve as a guide in understanding workflow scheduling techniques on computing systems such as Cluster, Supercomputers, Grid computing, Cloud computing, Edge computing, Fog computing, and the practical realization of such methods. It offers a whole new perspective and holistic approach in understanding computing systems’ workflow scheduling. Expressing and exposing approaches for various process-centric cloud-based applications give a full coverage of most systems’ energy consumption, reliability, resource utilization, cost, and application stochastic computation. By combining theory with application and connecting mathematical concepts and models with their resource management targets, this book will be equally accessible to readers with both Computer Science and Engineering backgrounds. It will be of great interest to students and professionals alike in the field of computing system design, management, and application. This book will also be beneficial to the general audience and technology enthusiasts who want to expand their knowledge on computer structure.

This book introduces the theory and applications of metaheuristic algorithms. It also provides methods for solving practical problems in such fields as software engineering, image recognition, video networks, and in the oceans. In the theoretical section, the book introduces the information feedback model, learning-based intelligent optimization, dynamic multi-objective optimization, and multi-model optimization. In the applications section, the book presents applications of optimization algorithms to neural architecture search, fuzz testing, oceans, and image processing. The neural architecture search chapter introduces the latest NAS method. The fuzz testing chapter uses multi-objective optimization and ant colony optimization to solve the seed selection and energy allocation problems in fuzz testing. In the ocean chapter, deep learning methods such as CNN, transformer, and attention-based methods are used to describe ENSO prediction and image processing for marine fish identification, and to provide an overview of traditional classification methods and deep learning methods. Rich in examples, this book will be a great resource for students, scholars, and those interested in metaheuristic algorithms, as well as professional practitioners and researchers working on related topics.

This book uses automotive embedded systems as an example to introduce functional safety assurance and safety-aware cost optimization. The book explores functional safety assurance from the perspectives of verification, enhancement, and validation. The functional safety assurance methods implement a safe and efficient assurance system that integrates safety verification, enhancement, and validation. The assurance methods offered in this book could provide a reasonable and scientific theoretical basis for the subsequent formulation of automotive functional safety standards. The safety-aware cost optimization methods divide cost types according to the essential differences of various costs in system design and establish reasonable models based on different costs. The cost optimization methods provided in this book could give appropriate cost optimization solutions for the cost-sensitive automotive industry, thereby achieving effective cost management and control. Functional safety assurance methods and safety-aware cost optimization support each other and jointly build the architecture of functional safety design methodologies for automotive embedded systems. The work aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in hardware cost optimization and development cost optimization design methods based on ensuring functional safety in general.

This book will serve as a guide in understanding workflow scheduling techniques on computing systems such as Cluster, Supercomputers, Grid computing, Cloud computing, Edge computing, Fog computing, and the practical realization of such methods. It offers a whole new perspective and holistic approach in understanding computing systems’ workflow scheduling. Expressing and exposing approaches for various process-centric cloud-based applications give a full coverage of most systems’ energy consumption, reliability, resource utilization, cost, and application stochastic computation. By combining theory with application and connecting mathematical concepts and models with their resource management targets, this book will be equally accessible to readers with both Computer Science and Engineering backgrounds. It will be of great interest to students and professionals alike in the field of computing system design, management, and application. This book will also be beneficial to the general audience and technology enthusiasts who want to expand their knowledge on computer structure.

This book focuses on scheduling algorithms for parallel applications on heterogeneous distributed systems, and addresses key scheduling requirements – high performance, low energy consumption, real time, and high reliability – from the perspectives of both theory and engineering practice. Further, it examines two typical application cases in automotive cyber-physical systems and cloud systems in detail, and discusses scheduling challenges in connection with resource costs, reliability and low energy. The book offers a comprehensive and systematic treatment of high-performance, low energy consumption, and high reliability issues on heterogeneous distributed systems, making it a particularly valuable resource for researchers, engineers and graduate students in the fields of computer science and engineering, information science and engineering, and automotive engineering, etc. The wealth of motivational examples with figures and tables make it easy to understand.

This book focuses on scheduling algorithms for parallel applications on heterogeneous distributed systems, and addresses key scheduling requirements – high performance, low energy consumption, real time, and high reliability – from the perspectives of both theory and engineering practice. Further, it examines two typical application cases in automotive cyber-physical systems and cloud systems in detail, and discusses scheduling challenges in connection with resource costs, reliability and low energy. The book offers a comprehensive and systematic treatment of high-performance, low energy consumption, and high reliability issues on heterogeneous distributed systems, making it a particularly valuable resource for researchers, engineers and graduate students in the fields of computer science and engineering, information science and engineering, and automotive engineering, etc. The wealth of motivational examples with figures and tables make it easy to understand.

This book introduces the theory and applications of metaheuristic algorithms. It also provides methods for solving practical problems in such fields as software engineering, image recognition, video networks, and in the oceans. In the theoretical section, the book introduces the information feedback model, learning-based intelligent optimization, dynamic multi-objective optimization, and multi-model optimization. In the applications section, the book presents applications of optimization algorithms to neural architecture search, fuzz testing, oceans, and image processing. The neural architecture search chapter introduces the latest NAS method. The fuzz testing chapter uses multi-objective optimization and ant colony optimization to solve the seed selection and energy allocation problems in fuzz testing. In the ocean chapter, deep learning methods such as CNN, transformer, and attention-based methods are used to describe ENSO prediction and image processing for marine fish identification, and to provide an overview of traditional classification methods and deep learning methods. Rich in examples, this book will be a great resource for students, scholars, and those interested in metaheuristic algorithms, as well as professional practitioners and researchers working on related topics.