Using OPEN.xtrace and Architecture-Level Models to Predict Workload Performance on In-Memory Database Systems

Abstract

In-Memory Database Systems (IMDB) come into operation on highly dynamic on-premise and cloud environments. Existing approaches use classical modeling notations such as queuing network models (QN) to reflect performance on IMDB. Changes to workload or hardware come along with a recreation of entire models. At the same time, new paradigms for IMDB increase parallelism within database workload, which intensifies the effort to create and parameterize models. To simplify and reduce the effort for researchers and practitioners to model workload performance on IMDB, we propose the use of architecture level performance models and present a model creation process, which transforms database traces of SAP HANA to the Palladio Component Model (PCM). We evaluate our approach based on experiments using analytical workload. We receive prediction errors for response time and throughput below 4 %.