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

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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.