Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters

Xiaomin Zhu, Chuan He, Kenli Li, Xiao Qin

Abstract: Developing energy-efficient clusters not only can reduce power electricity cost but also can improve system reliability. Existing scheduling strategies developed for energy-efficient clusters conserve energy at the cost of performance. The performance problem becomes especially apparent when cluster computing systems are heavily loaded. To address this issue, we propose in this paper a novel scheduling strategy–adaptive energy-efficient scheduling or AEES–for aperiodic and independent real-time tasks on heterogeneous clusters with dynamic voltage scaling. The AEES scheme aims to adaptively adjust voltages according to the workload conditions of a cluster, thereby making the best trade-offs between energy conservation and schedulability. When the cluster is heavily loaded, AEES considers voltage levels of both new tasks and running tasks to meet tasks’ deadlines. Under light load, AEES aggressively reduces the voltage levels to conserve energy while maintaining higher guarantee ratios. We conducted extensive experiments to compare AEES with an existing algorithm–MEG, as well as two baseline algorithms–MELV, MEHV. Experimental results show that AEES significantly improves the scheduling quality of MELV, MEHV and MEG.