Contention-Aware Workload Placement for In-Memory Databases in Cloud Environments

Abstract

Big data processing is driven by new types of in-memory database systems. In this article, we apply performance modeling to efficiently optimize workload placement for such systems. In particular, we propose novel response time approximations for in-memory databases based on fork-join queuing models and contention probabilities to model variable threading levels and per-class memory occupation under analytical workloads. We combine these approximations with a nonlinear optimization methodology that seeks optimal load dispatching probabilities in order to minimize memory swapping and resource utilization. We compare our approach with state-of-the-art response time approximations using real data from an SAP HANA in-memory system and show that our models markedly improve accuracy over existing approaches, at similar computational costs.