Transferring pareto frontiers across heterogeneous hardware environments

Software systems provide user-relevant configuration options called features. Features affect functional and non-functional system properties, whereas selections of features represent system configurations. A subset of configuration space forms a Pareto frontier of optimal configurations in terms of multiple properties, from which a user can choose the best configuration for a particular scenario. However, when a well-studied system is redeployed on a different hardware, information about property value and the Pareto frontier might not apply. We investigate whether it is possible to transfer this information across heterogeneous hardware environments. We propose a methodology for approximating and transferring Pareto frontiers of configurable systems across different hardware environments. We approximate a Pareto frontier by training an individual predictor model for each system property, and by aggregating predictions of each property into an approximated frontier. We transfer the approximated frontier across hardware by training a transfer model for each property, by applying it to a respective predictor, and by combining transferred properties into a frontier. We evaluate our approach by modeling Pareto frontiers as binary classifiers that separate all system configurations into optimal and non-optimal ones. Thus we can assess quality of approximated and transferred frontiers using common statistical measures like sensitivity and specificity. We test our approach using five real-world software systems from the compression domain, while paying special attention to their performance. Evaluation results demonstrate that accuracy of approximated frontiers depends linearly on predictors' training sample sizes, whereas transferring introduces only minor additional error to a frontier even for small training sizes.