Self Aware Computing Systems

Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems. The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about their behaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions. This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering.

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.

This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions.

This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering.The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about their behaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions.

Bringing together an extensively researched area with an emerging research issue, Context-Aware Computing and Self-Managing Systems presents the core contributions of context-aware computing in the development of self-managing systems, including devices, applications, middleware, and networks. The expert contributors reveal the usefulness of context-aware computing in developing autonomous systems that have practical application in the real world. The first chapter of the book identifies features that are common to both context-aware computing and autonomous computing. It offers a basic definition of context-awareness, covers fundamental aspects of self-managing systems, and provides several examples of context information and self-managing systems. Subsequent chapters on context-awareness demonstrate how a context can be employed to make systems smart, how a context can be captured and represented, and how dynamic binding of context sources can be possible. The chapters on self-management illustrate the need for "implicit knowledge" to develop fault-tolerant and self-protective systems. They also present a higher-level vision of future large-scale networks. Through various examples, this book shows how context-aware computing can be used in many self-managing systems. It enables researchers of context-aware computing to identify potential applications in the area of autonomous computing. The text also supports researchers of autonomous computing in defining, modeling, and capturing dynamic aspects of self-managing systems.

This book constitutes the proceedings of the 5th International Conference on Context-Aware Systems and Applications, ICCASA 2016, held in Thu Dau Mot, Vietnam, in November 2016. The 22 revised full papers presented were carefully selected from 35 submissions and cover a wide spectrum in the area of Context-Aware-Systems (CAS). CAS is characterized by its self- facets such as self-organization, self-configuration, self-healing, self-optimization, self-protection, where context awareness used to dynamically control computing and networking functions. The overall goal of CAS is to realize nature-inspired autonomic systems that can manage themselves without direct human interventions.

We are increasingly seeing computer systems which are expected to function without operator intervention. This is perhaps acceptable for running computer networks or traffic lights; however, we are now seeing computer systems deployed to qualitatively influence human judgments such as rulings on legal disputes or fitness for work to evaluate disability benefits. In keeping with the precautionary principle, it is important that those who are developing this capability — technologists and scientists — think through its potential implications.The aim of this book is to explore the technological and social and implications of computers and robots becoming increasingly ‘aware’ of their environment and the people in it, and their being increasingly ‘self-aware’ of their own existence within it.The wide-ranging scope of the text covers three different angles of the concept of ‘the computer after me’: (1) the next generation of computationally powerful aware systems; (2) systems in which the computer is aware of qualitatively impact human concerns such as law, health and rules; and (3) computers and robots which are aware of themselves.

This book is a spin-off of a by-invitation-only workshop on self-* properties in complex systems held in summer 2004 in Bertinoro, Italy. The workshop aimed to identify the conceptual and practical foundations for modeling, analyzing, and achieving self-* properties in distributed and networked systems. Based on the discussions at the workshop, papers were solicited from workshop participants and invited from leading researchers in the field. Besides presenting sound research results, the papers also present visionary statements, thought-provoking ideas, and exploratory results. The 27 carefully reviewed revised full papers, presented together with a motivating introduction and overview, are organized in topical sections on self-organization, self-awareness, self-awareness versus self-organization, supporting self-properties, and peer-to-peer algorithms.