Abstrakt
Experiments are used in many modern systems to optimize their operation. Such experiment-driven systems are used in various fields, such as web-based systems, smart-* systems, and various self-adaptive systems.
There is a class of these systems that derive their data from running simulations or another type of computation, such as in digital twins, online planning using probabilistic model-checking, or performance benchmarking. To obtain statistically significant results, these systems must repeat the experiments multiple times.
As a result, they consume extensive computation resources. The GraalVM benchmarking project detects performance changes in the GraalVM compiler.
However, the benchmarking project has an extensive usage of computational resources and time. The doctoral research project proposed in this paper focuses on controlling the experiments with the goal of reducing computation costs.
The plan is to use statistical and machine learning approaches to predict the outcomes of experiments and select the experiments yielding more useful information. As an evaluation, we are applying these methods to the GraalVM benchmarking project; the initial results confirm that these methods have the potential to significantly reduce computation costs.