Karsten Molka

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—In-memory database systems are among the technological drivers of big data processing. In this paper we apply analytical modeling to enable efficient sizing of in-memory databases. We present novel response time approximations under online analytical processing workloads to model thread-level fork-join and per-class memory occupation. We combine these(More)
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(More)
—The recent growth of interest for in-memory databases poses the question on whether established prediction methods such as response surfaces and simulation are effective to describe the performance of these systems. In particular, the limited dependence of in-memory technologies on the disk makes methods such as simulation more appealing than in the past,(More)
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