CSR:Small:Sensor-driven Thermal-aware Autonomic Management of Instrumented Datacenters

CSR：小：传感器驱动的仪表数据中心热感知自主管理

• 批准号: 1117263
• 负责人: Dario Pompili
• 金额: $ 12万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117263&HistoricalAwards=false
• 关键词: CSR; Small; Sensor; driven; Thermal

Due to the increasing demands for computing and storage, energy consumption, heat generation, and cooling requirements have become critical concerns both in terms of the growing costs as well as their environmental and societal impacts. Thus, thermal awareness, which is the knowledge of local unevenness in heat generation and extraction rates, and hence, heat imbalance at various points inside a datacenter, is essential to maximize energy and cooling efficiency as well as to minimize server failure rates.The objectives of this research are to acquire knowledge about the heat imbalance at different regions inside a datacenter and to enable thermal-aware self-configuration and self-optimization of computing resources inside a datacenter. These objectives are aimed at increasing the energy and cooling efficiency and at decreasing equipment failure rates so to minimize both the impact on the environment and the Total Cost of Ownership (TCO) of datacenters. Specifically, the project focuses on designing autonomic adaptive sampling solutions for enabling self-organization of heterogeneous sensors - composed of thermal cameras, scalar temperature and humidity sensors, and airflow meters - into a multi-tier sensing infrastructure, and on studying proactive, Quality of Service (QoS)-aware, heat-imbalance-based solutions for Virtual Machine (VM) consolidation and cooling system optimization in a virtualized air-cooled datacenter.This project will also result in the generation of computer-literate undergraduate and graduate researchers with a comprehensive knowledge of complex optimization problems in energy-efficient design and management of large datacenters. The PI will create new teaching modules on distributed sensing, provide opportunities for exchange programs, leverage existing minority student outreach networks at Rutgers, and incorporate student exchange programs as well as team-teaching approaches.

由于对计算和存储的需求不断增长，能源消耗，发热和冷却需求已成为不断增长的成本及其环境和社会影响的关键问题。因此，热意识是对热量产生和提取速率的局部不均匀性的了解，因此，在数据中心内部各个点的热量不平衡，对于提高能量和冷却效率以及服务器失败率以及最小化服务器的目的是至关重要的。计算数据中心内的资源。这些目标旨在提高能源和冷却效率，并降低设备故障率，从而最大程度地减少对环境的影响和数据中心总拥有成本（TCO）的影响。 Specifically, the project focuses on designing autonomic adaptive sampling solutions for enabling self-organization of heterogeneous sensors - composed of thermal cameras, scalar temperature and humidity sensors, and airflow meters - into a multi-tier sensing infrastructure, and on studying proactive, Quality of Service (QoS)-aware, heat-imbalance-based solutions for Virtual Machine (VM) consolidation and cooling在虚拟化的气冷数据中心中进行系统优化。该项目还将导致计算机识字的本科和研究生研究人员的生成，并全面了解大型数据中心的能源效率设计和管理中的复杂优化问题。 PI将创建有关分布式感应的新教学模块，为交换计划提供机会，利用Rutgers的现有少数学生推广网络，并结合学生交流计划以及团队教学方法。