Abstract
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 hori-zon 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 re-sults 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.
Original language | English |
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Title of host publication | 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2016 - Proceedings |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9781450340922 |
DOIs | |
Publication status | Published - 27 Feb 2016 |
Externally published | Yes |
Event | 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2016 - Barcelona, Spain Duration: 12 Mar 2016 → 16 Mar 2016 |
Conference
Conference | 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2016 |
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Country/Territory | Spain |
City | Barcelona |
Period | 12/03/16 → 16/03/16 |