Kinesthetics eXtreme: An External Infrastructure for Monitoring Distributed Legacy Systems

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

Kinesthetics eXtreme: An External Infrastructure for Monitoring Distributed Legacy Systems
Kaiser, Gail E.
Parekh, Janak J.
Gross, Philip N.
Valetto, Giuseppe
Technical reports
Computer Science
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Columbia University Computer Science Technical Reports
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Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is widely believed to be a promising solution to ever-increasing system complexity and the spiraling costs of human system 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 new and legacy components involving disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand or modify the code, and in many cases even when it is impossible to recompile. We present a meta-architecture implemented as active middleware infrastructure to explicitly add autonomic services via an attached feedback loop that provides continual monitoring and, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, as well as the full infrastructure, for use with a large variety of legacy, new systems, and systems of systems. We summarize several experiments spanning multiple domains.
Computer science
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