Retrofitting Autonomic Capabilities onto Legacy Systems

Janak J. Parekh; Gail E. Kaiser; Philip N. Gross; Giuseppe Valetto

Retrofitting Autonomic Capabilities onto Legacy Systems
Parekh, Janak J.
Kaiser, Gail E.
Gross, Philip N.
Valetto, Giuseppe
Computer Science
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Columbia University Computer Science Technical Reports
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Department of Computer Science, Columbia University
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New York
Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is a promising solution to ever-increasing system complexity and the spiraling costs of human management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build 'systems of systems' that draw from a gamut of disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand, modify or even recompile the target system's code. We present an autonomic infrastructure that operates similarly to active middleware, to explicitly add autonomic services to pre-existing systems via continual monitoring and a feedback loop that performs, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, independent of the rest of the full infrastructure, for use with a large variety of target systems. This work has been validated by several case studies spanning multiple application domains.
Computer science
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Suggested Citation:
Janak J. Parekh, Gail E. Kaiser, Philip N. Gross, Giuseppe Valetto, , Retrofitting Autonomic Capabilities onto Legacy Systems, Columbia University Academic Commons, .

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