Efficient Memory Occupancy Models for In-memory Databases

Predicting memory occupancy during the execution of large-scale analytical workloads becomes critical for in-memory databases. In particular, probabilistic performance measures for such systems are of interest, but difficult to model with analytical methods due to the highly variable threading levels in corresponding workloads. Since literature with queueing theoretic background largely ignores the memory modeling part, we propose a new probabilistic model to capture the memory occupancy distribution in such systems. We further combine this model with our analytical formulation TP-AMVA for greater efficiency compared to simulation and evaluate against experiments using SAP HANA.