Energy-efficient scheduling of compute-intensive video processing tasks

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Power consumption for video services is currently a serious concern that will only increase as the number of users is certain to continue its rapid rise (mobile data traffic is expected to grow 40-fold from 2010 to 2015, and mobile video is expected to account for 68.5% of that traffic in the U.S.). The main objective of this research program is to investigate and discover solutions to significantly reduce the energy consumption of compute-intensive video processing tasks, such as video compression and transcoding. The applicant and his team will achieve this objective through three research activities. The first will create novel methodologies and software tools to measure how a video task's performance (computational complexity, energy consumption and video quality) is affected by video processing parameters (e.g. the motion estimation algorithm), video content parameters (e.g. number of frames, resolution) and platform parameters (e.g. type of processor). The second will analyze the results and elaborate mathematical models to predict a video task's performance as a function of these various parameters. Various machine learning techniques (such as clustering and support vector machines) will be exploited in this research to elaborate these video processing performance models. The third will create an energy-efficient scheduler capable of near-optimally distributing video processing tasks on a computer cluster. The optimality criterion will be based on the desired compromise between video quality and power consumption, subject to constraints such as the time to complete the task, minimum visual quality and maximum power consumption. The video processing performance models will be crucial for improving scheduler performance, in terms of identifying the processing parameters that provide the best compromise while at the same time meeting the constraints. The latest optimization methods will be exploited in this project to create this energy-efficient scheduler. The team will validate their innovations on servers from two video companies with which they collaborate on other research projects. This new proposal, which focuses on power consumption, could, if successful, generate ideas to propel their partners still further.