Limit search to available items
Book Cover
E-book

Title Self-aware computing systems / Samuel Kounev, Jeffrey O. Kephart, Aleksandar Milenkoski, Xiaoyun Zhu, editors
Published Cham, Switzerland : Springer, 2017
Online access available from:
Springer eBooks    View Resource Record  

Copies

Description 1 online resource
Contents Preface; Background; Content; Intended Readership; Contents; Contributors; Part I Introduction; 1 The Notion of Self-aware Computing; 1.1 Introduction; 1.2 Definition of Self-aware Computing; 1.3 Previous Initiatives in Self-aware Computing; 1.3.1 Self-awareness in Artificial Intelligence; 1.3.2 Engineering Self-aware Systems; 1.3.3 Self-awareness in Pervasive Computing; 1.3.4 Systems with Decentralized Self-awareness; 1.3.5 Computational Self-awareness; 1.4 A Concept of a Self-aware Learning and Reasoning Loop; 1.5 Conclusion; References
2 Self-aware Computing Systems: Related Concepts and Research Areas2.1 Introduction; 2.2 Control; 2.3 Artificial Intelligence; 2.3.1 Overview of Agents and Multi-agent Systems; 2.3.2 Comparison with Self-aware Computing; 2.4 Autonomic Computing; 2.5 Organic Computing; 2.6 Service-Based Systems and Cloud Computing; 2.6.1 Service-Based Systems; 2.6.2 Cloud Computing; 2.6.3 Comparison with Self-aware Computing; 2.7 Self-organizing Systems; 2.7.1 Overview of Self-organizing Systems; 2.7.2 Cross-pollination Opportunities with Self-aware Computing; 2.8 Self-adaptive Systems
2.8.1 Overview of Basic Self-adaptive Systems2.8.2 Anticipatory Self-adaptive Systems; 2.9 Reflective Computing; 2.10 Models@run.time and Reflection; 2.11 Situation-Aware Systems and Context Awareness; 2.12 Symbiotic Cognitive Computing; 2.13 Auto-tuning; 2.14 Constructive Definition; 2.15 Summary; References; 3 Towards a Framework for the Levels and Aspects of Self-aware Computing Systems; 3.1 Introduction; 3.1.1 Why Consider Types of Self-awareness in Computing Systems?; 3.1.2 Summary of This Chapter; 3.2 Fundamentals, Inspiration, and Interpretations in Computing
3.2.1 What Is Self-awareness?3.2.2 Interpretations and Applications; 3.3 A Conceptual Framework; 3.3.1 Overarching Levels of Self-awareness; 3.3.2 Aspects of Reflective and Meta-reflective Self-awareness; 3.3.3 Domain of Self-awareness; 3.3.4 Putting It All Together; 3.4 Self-awareness and Goals; 3.5 Challenges; References; 4 Reference Scenarios for Self-aware Computing; 4.1 Introduction; 4.2 Rationale; 4.3 Adaptive Sorting; 4.3.1 Scenario; 4.3.2 Key Questions; 4.4 Data Center Resource Management; 4.4.1 Scenario; 4.4.2 Key Questions; 4.5 Cyber-Physical Systems; 4.5.1 Thermostat
4.5.2 Smart Home4.5.3 Smart Micro-grid; 4.5.4 System of Autonomous Shuttles; 4.6 Conclusion; References; Part II System Architectures; 5 Architectural Concepts for Self-aware Computing Systems; 5.1 Introduction; 5.2 Preliminaries; 5.2.1 Running Example: Smart Home; 5.2.2 Architectural Modeling with UML; 5.2.3 Self-awareness Terminology and Framework; 5.3 Architectural Elements for Self-awareness; 5.3.1 System, Environmental Context, and Modules; 5.3.2 Reflective and Prereflective Processes; 5.3.3 Awareness Models, Empirical Data (Models), and Goal Models
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
Notes Print version record
Subject Artificial intelligence.
Self-organizing systems.
Form Electronic book
Author Kephart, Jeffrey O., editor
Kounev, Samuel, editor
Milenkoski, Aleksandar, editor
Zhu, Xiaoyun, editor
ISBN 331947474X (electronic bk.)
9783319474748 (electronic bk.)
(print)
(print)