A Renewable Energy Driven Approach for Computational Sprinting

Computational Sprinting, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials, has proven to be an effective way to boost the computing performance for bursty workloads. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to make full use of Computational Sprinting. However, the intermittent nature of renewable energy, along with limited cooling capacity, makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting under the intermittent and time-varying nature of renewable energy supply. Three basic strategies are designed to determine the core count and frequency level for sprinting based on current power supply. Furthermore, we propose a Hybrid strategy that combines reinforcement learning to dynamically determine the optimal server setting, targeting at both the power provision safety and the quality of service. In consideration of practical cooling conditions, we also present a thermal-aware sprinting strategy Hybrid-T. Finally, we build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.