Recent years have seen many well-recognized energy conservation schemes developed for big data computing infrastructures, which aggregate heavy workloads on either a few chips or devices. While both of these methods reduce energy consumption, they can also elevate temperature levels on long standing IT instruments and ultimately cause them to overheat. As a consequence, the reliability of these devices can be significantly degraded as shown in many recent studies. In the worst cases, they can fail or malfunction. There is an imperative need for developing new power and energy control solutions in the multibillion-dollar industry of big data computing.
This research project is directed towards system-level solutions for temperature constrained data center energy management. It will develop methods and tools for controlling energy consumption in high-performance data centers operating under various temperature constraints by exploring the use of fuzzy decision-making techniques. Most of the existing studies rely on a well-developed relationship model between each criterion including open-loop search and optimization methods and rigid control schemes with fixed temperature constraint assumptions. In contrast, this project pursues solutions from a different angle: given that each criterion is in a non-linear relationship with another in a data center, how can new modeless control techniques that do not rely on fixed constraints work successfully? The project, if successful, will achieve foreseeable societal gains in terms of environmental benefits of energy conservation, potential for commercial impact of reducing costs and increasing operational efficiency in both warehouse-scale computer systems and data centers and reducing the data-center temperature to maintain reliability.
