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 effi-
ciency compared to simulation and evaluate against experiments
using SAP HANA.