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

由于对计算和存储的需求不断增长，能源消耗、发热和冷却要求已成为日益增长的成本及其环境和社会影响方面的关键问题。因此，热感知，其是热生成和提取率的局部不均匀性的知识，并且因此是数据中心内的各个点处的热不平衡的知识，对于最大限度地提高能源和冷却效率以及最大限度地降低服务器故障率至关重要。本研究的目标是获得有关数据中心内不同区域的热不平衡的知识，并实现热感知的自我配置和自我管理。优化数据中心内的计算资源。这些目标旨在提高能源和冷却效率，并降低设备故障率，从而最大限度地减少对环境的影响和企业的总拥有成本（TCO）。具体来说，该项目的重点是设计自主自适应采样解决方案，以实现异构传感器（由热成像相机，标量温度和湿度传感器和气流计组成）的自组织，并研究主动的，服务质量（QoS）感知，基于热不平衡的解决方案，用于虚拟化风冷数据中心中的虚拟机（VM）整合和冷却系统优化。该项目还将产生计算机-有文化的本科生和研究生研究人员，对大型发电厂的节能设计和管理中的复杂优化问题有全面的了解。PI将创建新的分布式传感教学模块，提供交流项目的机会，利用罗格斯大学现有的少数民族学生外展网络，并将学生交流项目和团队教学方法结合起来。

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.