Towards Dynamic Pricing-Based Collaborative Optimizations for Green Data Centers

Increased demand for cloud computing services has ushered power management schemes into the frontlines of data center research. Meanwhile, market penetration of intermittent renewable energy sources (e.g., wind and solar) is on the rise. While clean and abundant, their intermittency is troubling for utility companies, requiring power balancing reserves to be deployed at anytime to precisely match consumer demand with energy availability. However, a transformative redesign of our power grid is looming, calling for the use of dynamic energy pricing to resolve this issue by possibly shaping demand. Data centers, being significant consumers with the ability to adjust power utilization in real-time (e.g., by migrating its jobs to and from other locations), are ideal candidates to participate in dynamic pricing markets. We propose a collaborative cost optimization framework by coupling utilities with data centers via dynamic pricing. We develop models describing the information exchange framework for utilities and data centers and employ a distributed constraint optimization solver, Cologne, to negotiate a mutually optimal price. An evaluation of our system has been performed using real intermittent-energy-generation trace data. Modeling the dynamic price over this trace, we show that our technique could reduce a participating data center's costs by 75%. On the side of utilities, we further show that consumer power demand can be shaped to reveal a 17% improvement on average.