Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis

Highly configurable software systems, such as mobile apps, compilers, and big data engines, are increasingly exposed to end users and developers on a daily basis for varying use cases. Users are interested not only in the fastest configuration, but also in whether the fastest configuration for their applications also remains the fastest when the environmental situation has been changed. For instance, a mobile developer might be interested to know if the software that she has configured to consume minimal energy on a testing platform will also remain energy efficient on the users' mobile platform; or, in general, whether the configuration will remain optimal when the software is used in a different environment (e.g., with a different workload, on different hardware). Performance models have been extensively used to learn and describe the performance behavior of configurable systems [15], [19], [21], [23], [33], [43]-[45], [54], [61], [63]. However, the exponentially growing configuration space, complex interactions, and unknown constraints among configuration options [56] often make it costly and difficult to learn an accurate and reliable performance model. Even worse, existing techniques usually consider only a fixed environment (e.g., fixed workload, fixed hardware, fixed versions of the dependent libraries); should that environment change, a new performance model may need to be learned from scratch. This strong assumption limits the reusability of performance models across environments.