Proceedings Of The 6th Workshop On Principles And Practice Of Consistency For Distributed Data

Proceedings of the 6th Workshop on Principles and Practice of Consistency for Distributed Data

Dependable Computing

Dependable Computing Covering dependability from software and hardware perspectives Dependable Computing: Design and Assessment looks at both the software and hardware aspects of dependability. This book: Provides an in-depth examination of dependability/fault tolerance topics Describes dependability taxonomy, and briefly contrasts classical techniques with their modern counterparts or extensions Walks up the system stack from the hardware logic via operating systems up to software applications with respect to how they are hardened for dependability Describes the use of measurement-based analysis of computing systems Illustrates technology through real-life applications Discusses security attacks and unique dependability requirements for emerging applications, e.g., smart electric power grids and cloud computing Finally, using critical societal applications such as autonomous vehicles, large-scale clouds, and engineering solutions for healthcare, the book illustrates the emerging challenges faced in making artificial intelligence (AI) and its applications dependable and trustworthy. This book is suitable for those studying in the fields of computer engineering and computer science. Professionals who are working within the new reality to ensure dependable computing will find helpful information to support their efforts. With the support of practical case studies and use cases from both academia and real-world deployments, the book provides a journey of developments that include the impact of artificial intelligence and machine learning on this ever-growing field. This book offers a single compendium that spans the myriad areas in which dependability has been applied, providing theoretical concepts and applied knowledge with content that will excite a beginner, and rigor that will satisfy an expert. Accompanying the book is an online repository of problem sets and solutions, as well as slides for instructors, that span the chapters of the book.

Artificial Intelligence for Cloud-Native Software Engineering

Artificial intelligence is transforming software engineering by automating development, testing, deployment, and security processes, leading to more efficient and high-quality software solutions. AI-powered tools enhance scalability, reliability, and real-time analytics, enabling businesses to optimize operations and improve decision-making. As cloud-native architectures gain traction, AI-driven innovations are reshaping the way software is designed, maintained, and evolved, driving a new era of intelligent and adaptive technology solutions. Artificial Intelligence for Cloud-Native Software Engineering explores the transformative impact of AI on the software engineering lifecycle, highlighting its role in automating and enhancing various stages of software development. It provides a comprehensive overview of how AI technologies can assist software architects and engineers in creating high-quality, enterprise-grade software efficiently. Covering topics such as source code creation, data security, and multiparameter optimization, this book is an excellent resource for software engineers, computer scientists, professionals, researchers, scholars, academicians, and more.