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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 and non-linear optimization to help cloud...
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find...
Weighted round robin load balancing is a common routing policy offered in cloud load balancers. However, there is a lack of effective mechanisms to decide the weights assigned to each server to achieve an overall optimal revenue of the system. In this paper, we first experimentally explore the relation between probabilistic routing and weighted round robin load balancing policies. From the experiment...
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 approximations with a non-linear optimization program...
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