1. Aves: A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices
- Author
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Das, Roshan Bharath, Makkes, Marc X., Uta, Alexandru, Wang, Lin, Bal, Henri, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny, Computer Systems, Network Institute, High Performance Distributed Computing, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, and Ye, Yanfang Fanny
- Subjects
Decision support system ,Computer science ,Real-time computing ,Wearable computer ,020206 networking & telecommunications ,02 engineering and technology ,Simplicity (photography) ,Factor (programming language) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Power semiconductor device ,SDG 7 - Affordable and Clean Energy ,computer ,Energy (signal processing) ,computer.programming_language ,Efficient energy use - Abstract
Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves - a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine's improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications.
- Published
- 2020
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