Self-Aware Computing Systems / edited by Samuel Kounev, Jeffrey O. Kephart, Aleksandar Milenkoski, Xiaoyun Zhu.

Format:

Book

Contributor:

Kounev, Samuel, editor.

Kephart, Jeffrey O., editor.

Milenkoski, Aleksandar, editor.

Zhu, Xiaoyun, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Software engineering.

Computer networks.

Computers.

Software Engineering.

Computer Communication Networks.

Models and Principles.

Local Subjects:

Software Engineering.

Computer Communication Networks.

Models and Principles.

Physical Description:

1 online resource (XVIII, 722 pages) : 165 illustrations, 92 illustrations in color

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

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 range of 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.

Contents:

Part I Introduction

Part II System Architectures

Part III Methods and Algorithms

Part IV Applications and Case Studies

Part V Outlook.

Other Format:

Printed edition:

ISBN:

978-3-319-47474-8

9783319474748

Access Restriction:

Restricted for use by site license.