Keep calm and react with foresight: strategies for low latency and energy efficient elastic data stream processing
This paper addresses the problem of designing scaling strategies for elastic data stream processing. Elasticity allows applications to rapidly change their configuration on the fly (e.g. the amount of used resources) in response to dynamic workload fluctuations. In this work we face this problem by adopting the Model Predictive Control technique, a control theoretic method aimed at finding the optimal application configuration along a limited prediction horizon in the future by solving an online optimization problem. Our control strategies are designed to address latency constraints, using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support available in the modern multicore CPUs. The proactive capabilities, in addition to the latency and energy awareness, represent the novel features of our approach. To validate our methodology, we develop a thorough set of experiments on a high frequency trading application. The results demonstrate the high degree of flexibility and configurability of our approach, and show the effectiveness of our elastic scaling strategies compared with existing state of the art techniques used in similar scenarios.
PPoPP 2016