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Instrumentation and monitoring plays an important role in measurement-based performance analysis of software systems. However, in practice the performance overhead of extensive instrumentation is not negligible. Experiment-based performance analysis overcomes this problem through a series of experiments on selectively instrumented code, but requires additional manual effort to adjust required instrumentation and hence introduces additional costs. Automating the experiments and selective instrumentation can massively reduce the costs of performance analysis. Such automation, however, requires the capability of dynamically adapting instrumentation instructions. In this paper, we address this issue by introducing AIM, a novel instrumentation and monitoring approach for automated software performance analysis. We apply AIM to automate derivation of resource demands for an architectural performance model, showing that adaptable instrumentation leads to more accurate measurements compared to existing monitoring approaches.

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