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Finding Faster Configurations Using FLASH
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DOI：10.1109/TSE.2018.2870895.png)
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Abstract
En 中文
Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces Flash, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. Flash scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. Flash runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate Flash using 30 scenarios based on 7 software systems to demonstrate that Flash saves effort in 100 and 80 percent of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.
Keywords:
Software systems
Optimization
Throughput
Storms
Task analysis
Cloud computing
Performance prediction
search-based SE
configuration
multi-objective optimization
sequential model-based methods
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