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加，能源消耗、发热量和冷却需求已成为日益增长的成本及其对环境和社会影响的关键问题。因此，热感知对于最大化能量和冷却效率以及最小化服务器故障率至关重要。热感知是关于数据中心内不同区域的热不平衡的知识，从而实现数据中心内计算资源的热感知自我配置和自我优化。这些目标旨在提高能源和冷却效率，降低设备故障率，从而最大限度地减少对环境的影响和数据中心的总拥有成本(TCO)。具体地说，该项目专注于设计自主自适应采样解决方案，以使不同的传感器(由热像仪、标量温湿度传感器和空气流量计组成)能够自组织到多层传感基础设施中，并研究基于热不平衡的主动、服务质量(QoS)感知的解决方案，用于虚拟风冷数据中心中的虚拟机(VM)整合和冷却系统优化。该项目还将培养精通计算机的本科生和研究生研究人员，他们全面了解大型数据中心节能设计和管理中的复杂优化问题。PI将创建新的分布式传感教学模块，提供交换计划的机会，利用罗格斯大学现有的少数族裔学生外展网络，并纳入学生交换计划以及团队教学方法。

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.