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The software underpinning today's IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. The authors describe a software framework that achieves such adaptiveness for IT systems whose components can be modeled as Markov chains. The framework comprises an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and a method for developing instances of this architecture for real-world systems.
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