Energy-efficient scheduling of compute-intensive video processing tasks

视频服务的功耗目前是一个严重的问题，随着用户数量肯定会继续快速增长，功耗只会增加（预计从2010年到2015年，移动的数据流量将增长40倍，预计移动的视频将占美国流量的68.5%）。该研究计划的主要目标是调查和发现解决方案，以显着降低计算密集型视频处理任务（如视频压缩和转码）的能耗。申请人及其团队将通过三项研究活动实现这一目标。第一个将创建新的方法和软件工具来测量视频任务的性能（计算复杂度，能耗和视频质量）如何受到视频处理参数（例如运动估计算法），视频内容参数（例如帧数，分辨率）和平台参数（例如处理器类型）的影响。第二部分将分析结果，并详细阐述数学模型，以预测视频任务的性能作为这些不同参数的函数。各种机器学习技术（如聚类和支持向量机）将被利用在这项研究中阐述这些视频处理性能模型。第三个将创建一个节能的调度程序，能够在计算机集群上接近最佳地分配视频处理任务。最优性标准将基于视频质量和功耗之间的期望折衷，受到诸如完成任务的时间、最低视觉质量和最大功耗等约束。视频处理性能模型对于提高调度器性能至关重要，因为它可以识别提供最佳折衷方案同时满足约束条件的处理参数。最新的优化方法将被利用在这个项目中，以创建这个节能调度器。该团队将在两家视频公司的服务器上验证他们的创新，他们与两家视频公司合作开展其他研究项目。这项专注于功耗的新提案如果成功，可能会产生进一步推动合作伙伴的想法。

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.