摘要
如今日益增长的数据中心能耗,特别是冷却系统能耗已日益受到重视,降低系统能耗能够减少数据中心碳排放.提出了一种基于模型预测控制(model prediction control,简称MPC)的节能调度策略,该策略可以有效地减小数据中心冷却能耗.该方法采用动态电压频率调节技术来调整计算节点频率,从而减少节点间的热循环;所有节点的峰值温度可被保持在温度阈值下,在任务的执行中稳态误差较小.该方法可以通过动态频率调节来抑制由于负载类型变化造成的模型不确定性带来的内部扰动,分析结果表明,基于模型预测的温控算法系统开销较小,具有良好的可扩展性.基于该算法设计的控制器能够有效地降低输入温度,提高数据中心能耗效率.通过在实际数据中心内运行的模拟网上书店,该方法与安全最小热传递算法和传统反馈温控算法这两种经典方法相比,无论是在正常条件下还是在扰动存在的情况下都能取得较好的温度抑制效果,系统性能如吞吐率也达到最大.在相同的负载条件下,该方法能够获得最小的输入峰值温度和最小的冷却能耗.
Today the ever-growing energy cost, especially cooling cost of data centers, draws much attention for carbon emission reduction. This paper presents an energy efficient scheduling strategy based on model prediction control（MPC） to reduce cooling cost in data centers. It uses dynamic voltage frequency scaling technology to adjust the frequencies of computing nodes of a cluster in a way to minimize heat recirculation effect among the nodes. The maximum inlet temperature of nodes can be kept under temperature limits with little stable error. The method can also deal with inner disturbance（system model variation） by dynamic frequencies regulation among the nodes. Analysis shows good scalability and small overhead, making the method applicable in huge data centers. A temperature-aware controller is designed to reduce inlet temperatures to improve energy efficiency of data centers. Using a simulated online bookstore run in a heterogeneous data center the proposed method is proved to have larger throughput in both normal and emergency cases compared with existing solutions such as safe least recirculation heat temperature controller and traditional feedback temperature controller. The MPC-based scheduling method also has less inlet temperature and cooling cost comparing with those two methods under same workload.
出处
《软件学报》
EI
CSCD
北大核心
2017年第2期429-442,共14页
Journal of Software
基金
国家自然科学基金(61003185)
湖北省自然科学基金(201FFB04505)~~
关键词
模型预测控制
反馈控制
热传递
阈值温度
内部扰动
能耗效率
model prediction control
feedback control
heat recirculation
temperature threshold
inner disturbance
energy efficiency
