Learning a Data Center Model for Efficient Demand Response

Data center demand is projected to increase dramatically over the coming decades, creating concerns about their carbon footprint and motivating the design of methods that can scale data center capabilities sustainably. One such method is Demand Response (DR), which provides incentives for power consumers to regulate their consumption in compliance with sustainability and capacity needs in the grid. One limitation of existing work in data center DR is the computational expense of generating accurate average power estimates and flexibility forecasts for a data center, given knowledge about the data center's configuration and activity. We introduce CONDOR, a machine learning (ML) method to learn the relationship between a data center's configuration (e.g., power, performance, and load characteristics) and an objective function incorporating DR savings, energy cost, and workload quality-of-service (QoS) compliance. CONDOR optimizes power and flexible reserve estimates that minimize an objective function, helping the data center efficiently meet compliance with sustainability measures. Our results demonstrate that CONDOR achieves speed increases of around 15,000x in computing accurate forecasts compared to simulation-based estimation, which enables DR participation of large real-world data centers in DR programs without debilitating computational overhead.