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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)
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)
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(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)
Systems for processing large scale analytical workloads are increasingly moving from on-premise setups to on-demand configurations deployed on scalable cloud infrastructures. To reduce the cost of such infrastructures, existing research focuses on developing novel methods for workload and server consolidation. In this paper, we combine analytical modeling(More)
The rapid increase in the demand for cloud computing is driving a more recent increasing move towards multi-cloud environments by infrastructure providers. However, one of the main challenges for infrastructure providers is in sustaining profitability while provisioning services and components in such multi-cloud environments. This poster paper presents(More)
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