Energy-efficient scheduling of compute-intensive video processing tasks

计算密集型视频处理任务的节能调度

• 批准号: 327278-2012
• 负责人: Coulombe, Stéphane
• 金额: $ 1.31万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=595216
• 关键词: Energy; efficient; scheduling; compute; intensive

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.

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，这个问题只会增加（预计从2010年到2015年，移动数据流量将增长40倍，移动视频预计将占美国流量的68.5%）。本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。申请人和他的团队将通过三个研究活动来实现这一目标。第一个将创建新的方法和软件工具来衡量视频任务的性能（计算复杂性，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。本研究将利用各种机器学习技术（如聚类和支持向量机）来阐述这些视频处理性能模型。第三个将创建一个节能调度器，能够在计算机集群上近乎最佳地分配视频处理任务。最优标准将基于视频质量和功耗之间的理想折衷，并受到完成任务所需时间、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，因为它可以确定在满足约束条件的同时提供最佳折衷的处理参数。该项目将利用最新的优化方法来创建这个节能调度程序。该团队将在两家视频公司的服务器上验证他们的创新，他们与这两家公司合作开展其他研究项目。这项新提议的重点是电力消耗，如果成功，可能会产生新的想法，推动他们的合作伙伴走得更远。