Self-Managing Systems: A Control Theory Foundation

Yixin Diao; Joseph L. Hellerstein; Sujay Parekh; Rean Griffith; Gail E. Kaiser; Dan Phung

Self-Managing Systems: A Control Theory Foundation
Diao, Yixin
Hellerstein, Joseph L.
Parekh, Sujay
Griffith, Rean
Kaiser, Gail E.
Phung, Dan
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
The high cost of ownership of computing systems has resulted in a number of industry initiatives to reduce the burden of operations and management. Examples include IBM's Autonomic Computing, HP's Adaptive Infrastructure, and Microsoft's Dynamic Systems Initiative. All of these efforts seek to reduce operations costs by increased automation, ideally to have systems be self-managing without any human intervention (since operator error has been identified as a major source of system failures). While the concept of automated operations has existed for two decades, as a way to adapt to changing workloads, failures and (more recently) attacks, the scope of automation remains limited. We believe this is in part due to the absence of a fundamental understanding of how automated actions affect system behavior, especially system stability. Other disciplines such as mechanical, electrical, and aeronautical engineering make use of control theory to design feedback systems. This paper uses control theory as a way to identify a number of requirements for and challenges in building self-managing systems, either from new components or layering on top of existing components.
Computer science
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Suggested Citation:
Yixin Diao, Joseph L. Hellerstein, Sujay Parekh, Rean Griffith, Gail E. Kaiser, Dan Phung, , Self-Managing Systems: A Control Theory Foundation, Columbia University Academic Commons, .

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