SBSelector: Search Based Component Selection for Budget Hardware

Abstract

Determining which functional components should be integrated to a large system is a challenging task, when hardware constraints, such as available memory, are taken into account. We formulate such problem as a multi-objective component selection problem, which searches for feature subsets that balance the provision of maximal functionality at minimal memory resource cost. We developed a search-based component selection tool, and applied it to the KDE-based application, Kate, to find a set of Kate instantiations that balance functionalities and memory consumption. Our results report that, compared to the best attainment of random search, our approach can reduce at most $23.70\,\%$ memory consumption with respect to the same number components. While comparing to greedy search, the memory reduction can be up to $19.04\,\%$. SBSelector finds a instantiation of Kate that provides 16 more components, while only increasing memory by $1.7\,\%$.