In-memory database systems are among the technological
drivers of big data processing. In this paper we
apply analytical modeling to enable efficient sizing of in-memory
databases. We present novel response time approximations under
online analytical processing workloads to model thread-level forkjoin
and per-class memory occupation.We combine these approximations
with a non-linear optimization program to minimize
memory swapping in in-memory database clusters. We compare
our approach with state-of-the-art response time approximations
and trace-driven simulation using real data from an SAP HANA
in-memory system and show that our optimization model is
significantly more accurate than existing approaches at similar
computational costs.