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Molka, Karsten; Casale, Giuliano (2016)
Publisher: ACM
Journal: ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Languages: English
Types: Article,Unknown
Subjects:
Identifiers:doi:10.1145/2961888
Big data processing is driven by new types of in-memory database systems. In this paper 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 work-loads. We combine these approximations with a non-linear optimization methodology that seeks for 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.
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