Software runtime monitoring with adaptive sampling rate to collect representative samples of execution traces
نویسندگان
چکیده
Monitoring software systems at runtime is key for understanding workloads, debugging, and self-adaptation. It typically involves collecting storing observable data, which can be analyzed online or offline. Despite the usefulness of system it may significantly impact execution by delaying response times competing with resources. The typical approach to cope this filter portions monitored sample data. Although these approaches are a step towards achieving desired trade-off between amount collected information on performance, they focus data particular type capture that does not correspond actual behavior. In response, we propose an adaptive monitoring process dynamically adapt sampling rate while systems. includes algorithms statistical foundations improve representativeness samples without compromising performance. Our evaluation targets five applications widely used benchmark. shows error (RMSE) our 9-54% lower than main alternative strategy (sampling inversely proportional throughput), 1-6% higher performance impact.
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ژورنال
عنوان ژورنال: Journal of Systems and Software
سال: 2023
ISSN: ['0164-1212', '1873-1228']
DOI: https://doi.org/10.1016/j.jss.2023.111708