CSR-DMSS, SM: Next-Generation Thermal-Aware, Energy-Efficient Resource Management for Data Centers

CSR-DMSS、SM：数据中心的下一代热感知、节能资源管理

• 批准号: 0834797
• 负责人: Sandeep Gupta
• 金额: $ 25万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0834797&HistoricalAwards=false
• 关键词: CSR; DMSS; SM; Next; Generation

This project addresses the challenges in developing predictive and autonomic thermal-aware and energy-efficient task scheduling algorithms for heterogeneous High-Performance Computing datacenters. Energy savings of software controlled job management in large-scale datacenters has not been adequately explored. Existing scheduling and power management algorithms are mostly reactive in nature. This research will investigate the trade-off between the schedule efficacy and the runtime complexity of producing a spatio-temporal task schedule (with start times of each task and its computing node assignment or placement), with an objective to develop algorithms that can be applied in real-world situations. Novelty of this research lies in viewing datacenters as a cyber-physical entity and proactively configuring and controlling both the cooling and computing systems in a coordinated manner. Previous research results show that energy savings are very limited unless the cooling is dynamically adjusted to the computing load. Outcomes of this research will include: a) study of the trade-off between computational complexity and efficiency of the scheduling algorithm, b) proactive algorithms that produce task, power and cooling control schedules in an unified manner, c) software architecture that will encompass the power control of the computing equipment, as well as dynamic configuration of the cooling systems, and d) test results from real-world data centers that show the applicability and practicality of the developed schemes. Results will be publicly available at IMAPCT Lab's website (http://impact.asu.edu/), and scientific findings will be included in multi-disciplinary courses with cyber-physical focus. Practical benefits of this research will be demonstrated in academic and corporate datacenters.

该项目解决了为异构高性能计算中心开发预测性和自主热感知和节能任务调度算法的挑战。在大规模的制造业中，软件控制的作业管理的节能还没有得到充分的探索。现有的调度和功率管理算法大多是被动的。本研究将调查之间的权衡的调度效率和运行时的复杂性产生的时空任务调度（每个任务的开始时间和计算节点的分配或放置），与目标开发的算法，可以应用于现实世界的情况。本研究的新奇之处在于将散热中心视为网络物理实体，并以协调的方式主动配置和控制冷却和计算系统。先前的研究结果表明，除非根据计算负载动态调整冷却，否则节能非常有限。这项研究的成果将包括：a）研究调度算法的计算复杂度和效率之间的折衷，B）以统一方式产生任务、功率和冷却控制调度的主动算法，c）将包含计算设备的功率控制以及冷却系统的动态配置的软件架构，以及d）来自真实世界数据中心的测试结果，其显示所开发的方案的适用性和实用性。研究结果将在IMAPCT实验室的网站（http：//impact.asu.edu/）上公布，科学发现将被纳入以网络物理为重点的多学科课程。这项研究的实际效益将在学术和企业的研究中心得到证明。