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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571968
• 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，只会随着用户数量肯定会迅速上升而增加（移动数据流量从2010年到2015年预计将增长40倍，而移动视频预计将占美国该流量的68.5％）。该研究计划的主要目的是调查和发现解决方案，以大大减少计算密集型视频处理任务的能源消耗，例如视频压缩和转编码。申请人及其团队将通过三项研究活动实现这一目标。第一个将创建新颖的方法论和软件工具，以测量视频处理参数（例如运动估计算法），视频处理参数（例如，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。第二个将分析结果并精心制作的数学模型，以预测视频任务的性能，这是这些各种参数的函数。在本研究中将利用各种机器学习技术（例如聚类和支持向量机），以详细说明这些视频处理性能模型。第三个将创建能够在计算机群集上近距离分发视频处理任务的节能调度程序。最佳标准将基于视频质量和功耗之间所需的妥协，但要受到诸如完成任务的时间，最小视觉质量和最大功耗的时间。视频处理性能模型对于确定提供最佳折衷的处理参数而同时满足约束的处理参数，对于改善调度程序的性能至关重要。该项目将利用最新的优化方法来创建此节能调度程序。该团队将从两家视频公司中验证他们在其他研究项目上合作的视频公司的创新。这项专注于功耗的新提案，如果成功的话，可以产生想法，以进一步推动其伴侣。