GreenSprint: Effective Computational Sprinting in Green Data Centers
Haoran Cai, Xu Zhou, Qiang Cao, Hong Jiang, Feng Sheng, Xiandong Qi, Jie Yao, Changsheng Xie, Liang Xiao, Liang Xian Gu · 2018
Computational Sprinting has proven to be an effective way to boost the computing performance for bursty workloads, 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. 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 exploit the maximal potential of Computational Sprinting. However, the intermittent nature of renewable energy 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. We present four sprinting strategies to address the challenge imposed by the intermittent and time-varying nature of renewable energy supply. 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.