Samuel Kounev – författare

This book serves as both a textbook and handbook on the benchmarking of systems and components used as building blocks of modern information and communication technology applications. It provides theoretical and practical foundations as well as an in-depth exploration of modern benchmarks and benchmark development.

This book serves as both a textbook and handbook on the benchmarking of systems and components used as building blocks of modern information and communication technology applications. It provides theoretical and practical foundations as well as an in-depth exploration of modern benchmarks and benchmark development.

The book is divided into two parts: foundations and applications. The first part introduces the foundations of benchmarking as a discipline, covering the three fundamental elements of each benchmarking approach: metrics, workloads, and measurement methodology. The second part focuses on different application areas, presenting contributions in specific fields of benchmark development. These contributions address the unique challenges that arise in the conception and development of benchmarks for specific systems or subsystems, and they demonstrate how the foundations and concepts in the first part of the book are being used in existing benchmarks. Further, the book presents a number of concrete applications and case studies based on input from leading benchmark developers from consortia such as the Standard Performance Evaluation Corporation (SPEC) and the Transaction Processing Performance Council (TPC). Besides a number of updates in almost all chapters, for this new edition three chapters are added in Part II of the book: (1) “Machine Learning and Artificial Intelligence” to cater the growing need to evaluate and benchmark ML and AI systems, (2) “Scalability of Networks and Systems” focusing on novel metrics and techniques to evaluate scalability, and (3) "PC, Workstation, Graphics, and Network Benchmarks“ covering popular benchmarks like SYSmark, PCMark, Phoronix Test Suite, 3DMark, the Blender benchmark, and end-to-end network performance tools.

Providing both practical and theoretical foundations, as well as a detailed discussion of modern benchmarks and their development, the book is intended as a handbook for professionals and researchers working in areas related to benchmarking. It offers an up-to-date point of reference for existing work as well as latest results, research challenges, and future research directions. It also can be used as a textbook for graduate and postgraduate students studying any of the many subjects related to benchmarking. While readers are assumed to be familiar with the principles and practices of computer science, as well as software and systems engineering, no specific expertise in any subfield of these disciplines is required.

This book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering. It describes architectures and algorithms for self-aware systems as well as the benefits and pitfalls of self-awareness, and reviews much of the latest relevant research across a wide array of disciplines, including open research challenges.

The chapters of this book are organized into five parts: Introduction, System Architectures, Methods and Algorithms, Applications and Case Studies, and Outlook. Part I offers an introduction that defines self-aware computing systems from multiple perspectives, and establishes a formal definition, a taxonomy and a set of reference scenarios that help to unify the remaining chapters. Next, Part II explores architectures for self-aware computing systems, such as generic concepts and notations that allow a wide rangeof self-aware system architectures to be described and compared with both isolated and interacting systems. It also reviews the current state of reference architectures, architectural frameworks, and languages for self-aware systems. Part III focuses on methods and algorithms for self-aware computing systems by addressing issues pertaining to system design, like modeling, synthesis and verification. It also examines topics such as adaptation, benchmarks and metrics. Part IV then presents applications and case studies in various domains including cloud computing, data centers, cyber-physical systems, and the degree to which self-aware computing approaches have been adopted within those domains. Lastly, Part V surveys open challenges and future research directions for self-aware computing systems.

It can be used as a handbook for professionals and researchers working in areas related to self-aware computing, and can also serve as an advanced textbook for lecturers and postgraduate students studying subjects like advanced software engineering, autonomic computing, self-adaptive systems, and data-center resource management. Each chapter is largely self-contained, and offers plenty of references for anyone wishing to pursue the topic more deeply.