Abstract
Advanced software systems can reconfigure themselves at run-time by choosing between alternative options for performing certain functions. Such options can be built into the systems, but also are externally available on open and uncontrolled platforms. Main examples are Web services and mashups on the Internet today. We show how run-time software self-adaptation with uncontrolled external options can be optimized by stopping theory, yielding the best possible lower probability bound for choosing an optimal option. We present two application scenarios and derive efficient optimization algorithms. We simulate examples for both scenarios, where we measure the improvement over an assumed closed software system.
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