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AUTONOMIC COMPUTING: ARCHITECTURE, APPLICATION AND CHALLENGES

Authors: Abhang Swati Ashok;

AUTONOMIC COMPUTING: ARCHITECTURE, APPLICATION AND CHALLENGES

Abstract

Advances in computing and communication technologies and software tools have resulted in an explosive growth in networked-applications and information services that cover all aspects of our life. These services and applications are inherently complex, dynamic and heterogeneous. Similarly, the underlying information infrastructures such as the Internet are complex, heterogeneous and dynamic. This combination exacerbates complexities related to application development, configuration and management, and makes current computing paradigms brittle and inefficient. As a result, applications, programming environments and information infrastructures are rapidly becoming unmanageable, insecure and inefficient when handling runtime changes. This has led researchers to consider alternative programming paradigms and management techniques that are based on strategies used by biological systems to deal with complexity, dynamism, heterogeneity and uncertainty. Autonomic computing is inspired by the human autonomic nervous system that handles complexity and uncertainties, and aims at realizing computing systems and applications capable of managing themselves with minimum human intervention

Keywords

Autonomic computing, Human Nervous System, Autonomic component, Autonomic Computing Paradigm

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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