  Efficiency of self-optimizing systems is heavily dependent on their
optimization strategies, e.g., choosing exact or approximate solver. A choice
of such a strategy, in turn, is influenced by numerous factors, such as
re-optimization time, size of the problem, optimality constraints, etc. Exact
solvers are domain-independent and can guarantee optimality but suffer from
scaling, while approximate solvers offer a "good-enough" solution in exchange
for a lack of generality and parameter-dependence. In this paper we discuss the
trade-offs between exact and approximate optimizers for solving a quality-based
software selection and hardware mapping problem from the scalability
perspective. We show that even a simple heuristic can compete with thoroughly
developed exact solvers under condition of an effective parameter tuning.
Moreover, we discuss robustness of the obtained algorithm's configuration. Last
but not least, we present a software product line for parameter tuning, which
comprise the main features of this process and can serve as a platform for
further research in the area of parameter tuning.
