Using probabilistic reasoning to automate software tuning (2003)
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@TECHREPORT{Sullivan03usingprobabilistic,
author = {David Gerard Sullivan},
title = {Using probabilistic reasoning to automate software tuning},
institution = {},
year = {2003}
}
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Abstract
Complex software systems typically include a set of parameters that can be adjusted to improve the system’s performance. System designers expose these parameters, which are often referred to as knobs, because they realize that no single configuration of the system can adequately handle every possible workload. Therefore, users are allowed to tune the system, reconfiguring it to perform well on a specific workload. However, manually tuning a software system can be an extremely difficult task, and it is often necessary to dynamically retune the system as workload characteristics change over time. As a result, many systems are run using either the default knob settings or non-optimal alternate settings, and potential performance improvements go unrealized. Ideally, the process of software tuning should be automated, allowing software systems to determine their own optimal knob settings and to reconfigure themselves as needed in response to changing conditions. This thesis demonstrates that probabilistic reasoning and decision-making techniques can be used as the foundation of an effective, automated approach to
