Today's IT systems with its ever-growing communication infrastructures and computing applications are becoming more and more large in scale, which results in exponential complexity in their engineering, operation, and maintenance. Conventional paradigms for run-time deployment, management, maintenance, and evolution are particularly challenged in tackling these immense complexities. Recently, it has widely been recognized that self-organization and self-management/regulation offer the most promising approach to addressing such challenges. Consequently, a number of autonomic/adaptive computing initiatives have been launched by major IT companies, like IBM, HP, and others.
Self-organization and adaptation are concepts stemming from the nature and have been adopted in systems theory. They are considered to be the essential ingredients of any living organism and, as such, are studied intensively in biology, sociology, and organizational theory. They have also penetrated into control theory, cybernetics and the study of adaptive complex systems. The original idea was to understand complex systems behaviour by understanding the systems' self-organization and adaptation mechanisms, i.e., to understand a system by observing the behaviour of its components and their interactions. However, as stated, the study of self-organization and adaptation has mainly been related to living systems so far.
Computing and communication systems are basically artificial systems. This prevents conventional self-organization and adaptation principles and approaches from being directly applicable to computing and communication systems. The complexity attributes in terms of openness, scalability, uncertainty, discrete-event dynamics, etc. have varied contexts in large-scale complex IT systems, and are too prominent to be solved by procedures pre-defined at off-time. Rather, they have to be tackled by means of run-time perception of the complexity patterns and the run-time enforcement of self-organization and adaptation policies. The current knowledge about large-scale complex IT systems is still very limited, and a framework has yet to be established for their self-organization and adaptation.
The methodology of multi-agent systems and the technology of Grid computing have shed lights for the exploration into the self-organization and adaptation of large-scale complex IT systems. A multi-agent system is one that consists of a collection of autonomous and intelligent agents that have the ability to interact with each other and, thus, may by themselves constitute organizations at run-time. The global behaviour of a multi-agent system stems from the emergent interactions among the agents. Multi-agent systems have been studied widely, not only in computer science, software engineering and artificial intelligence, but even more widely under the heading “systems theory” in economics, management science and sociology. In fact, multi-agent systems permeate social, economic, and technical domains. Essentially, multi-agent systems provide a generic model for large-scale complex IT systems. Exploring and understanding the self-organization and adaptation of multi-agent systems is of profound significance for engineering the self-organization and self-management/regulation of large-scale complex IT systems, comprised of communication infrastructures and computing applications.
Grid computing is the new generation of distributed and networked information and computing systems which have the capacity to enable users and applications, in an emergent manner, to transcend the organizational boundaries and to gain access to the distributed heterogeneous computing resources owned and administrated locally by different organizations. A Grid computing system is by nature a large-scale, complex, and open multi-agent system. Grid computing compounds various areas such as distributed computing resource management, semantic web technology, service-oriented architecture and service management, distributed workflow management, distributed problem solving environment, etc. A Grid commutating system exposes all the complexity attributes typical of large-scale complex IT systems. Investigating the self-organization and autonomic systems for Grid computing has remained a huge challenge.
This book provides in-depth thoughts about the above discussed challenges as well as a range of state-of-the-art methodologies and technologies for the entirely new area, that is, self-organization and autonomic systems in computing and communications. We refer to this newly emerging area as Self-Organization and Autonomic Informatics, which has represented the future generation of IT systems, comprised of communication infrastructures and computing applications, which are inherently large-scale, complex, and open.
The 16 full-length and 17 short papers included in this book are carefully selected from the 58 initial manuscripts based on a rigorous peer review process that was conducted by the 86 technical reviewers worldwide who are experts or active researchers in the related areas. The contents of the book are structured as five parts, i.e.,
Part I: Self-Organization and Adaptation in General;
Part II: Self-Organization/Adaptation of Multi-Agent Systems;
Part III: Self-Organization/Adaptation for Grid Computing;
Part IV: Autonomic Computing in General; and
Part V: Autonomic Communications.
We are sure that you will find the book interesting.
Editors: Professor Hans Czap, University of Trier, Germany; Professor Rainer Unland, University of Duisburg-Essen, Germany; Professor Cherif Branki, University of Paisley, UK; Professor Huaglory Tianfield, Glasgow Caledonian University, UK