教育部高等学校博士学科点专项科研基金(20134307110029); 湖南省自然科学基金（2015JJ3023); 西南电子电信技术研究室公开课题(2013001)
目前,节能已成为云数据中心的研究热点.建设节能的云数据中心不仅可以减少用电消耗,而且可以提高系统的可靠性.现有的云中心节能调度算法缺乏在任务调度级别的考虑,使得任务执行效果受到较大影响.为此,首先给出了一种基于滚动优化的实时任务调度器结构,然后详细分析和构建了任务能量消耗模型.在此基础上提出了一种实时非周期任务节能调度算法EARH(energy-aware scheduling algorithm).EARH采用的滚动优化策略能够被拓展并集成其他节能调度算法.此外,提出了资源动态增加与缩减策略,用于在系统可调度性与节能两方面进行权衡.最后,通过大量的模拟实验验证了EARH的性能.与其他3种基准算法相比,其实验结果表明,EARH的调度质量优于其他算法,可有效提高系统性能.
Nowadays, energy saving has become a focus in deploying clouds. Developing energy-aware cloud data centers can not only reduce power electricity cost but also improve system reliability. Existing scheduling algorithms developed for energy-aware cloud data centers are commonly lack of consideration of task level scheduling. To address this issue, this paper proposes a novel rolling-horizon scheduling architecture for real-time task scheduling, together with a detailed task energy consumption model. Based on the novel scheduling architecture, this work develops an energy-aware scheduling algorithm EARH (energy-aware scheduling algorithm) for real-time aperiodic tasks. EARH employs a rolling-horizon optimization policy and can be extended to integrate other energy-aware scheduling algorithms. Strategies for the resource scaling up and scaling down are also presented to make a good trade-off between task’s schedulability and energy saving. Extensive experiments are conducted to validate the superiority of EARH by comparing it with three baselines. The results show that EARH significantly improves the scheduling quality over the others and it is suitable for real-time task scheduling in virtulized cloud data centers.