NDLI: Prediction-Guided Performance-Energy Trade-off with Continuous Run-Time Adaptation

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Prediction-Guided Performance-Energy Trade-off with Continuous Run-Time Adaptation

Content Provider	ACM Digital Library
Author	Suh, G. Edward Lo, Daniel Song, Taejoon
Abstract	Recent work has demonstrated that prediction-guided DVFS control can significantly improve the energy efficiency of interactive applications with little to no impact on user experience when running in isolation. In this work, we propose to add an on-line learning capability to the execution-time predictor, which enables the predictor to automatically adapt to changes in the environment such as interference from other applications and be easily applied across diverse platforms. This paper introduces several techniques to address the overhead of performing on-line learning, including incremental training based on QR decomposition and explicit change detection for fast adaptation. In addition to the DVFS control, we show that the proposed prediction model can be used to intelligently select a core in a heterogeneous system. Experimental results on the ARM big.LITTLE platform show that our DVFS controller and core scheduler can effectively remove deadline misses even under significant interference from competing processes while consuming far lower energy compared to traditional schemes.
Starting Page	224
Ending Page	229
Page Count	6
File Format	PDF
ISBN	9781450341851
DOI	10.1145/2934583.2934598
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Execution time prediction Energy efficiency Dvfs On-line training
Content Type	Text
Resource Type	Article

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