ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (7): 1517-1531.doi: 10.7544/issn1000-1239.2016.20160133

所属专题: 2016绿色计算专题

• 系统结构 • 上一篇    下一篇

基于统计量的存储系统磁盘功耗建模方法研究

孙鉴,李战怀,张晓,王惠峰,赵晓南   

  1. (西北工业大学计算机学院 西安 710129) (qwert3277@163.com)
  • 出版日期: 2016-07-01
  • 基金资助: 
    国家“八六三”高技术研究发展计划基金项目(2013AA01A215);国家自然科学基金项目(61472323,61502392);中央高校基本科研业务费专项资金项目(3102015JSJ0009);华为创新基金项目(YB2014040023)

A Statistic-Based Method for Hard-Disk Power Consumption in Storage System

Sun Jian, Li Zhanhuai, Zhang Xiao, Wang Huifeng, Zhao Xiaonan   

  1. (School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710129)
  • Online: 2016-07-01

摘要: 大数据的迅猛发展导致数据中心的存储规模急剧扩张,由此引发的高能耗已经成为数据中心普遍面临的一个突出问题,磁盘类存储介质在数据中心耗能中所占的比例也在逐年增加,能耗建模在目前学者们的研究中越来越受到关注.精确的磁盘能耗模型不仅可以解决数据中心中的电力配套问题,而且为当前数据中心各种能耗管理技术体现更为精确的节能效果.提出了一种基于统计量的磁盘能耗预测模型,该模型弥补了传统细粒度模型产生的额外负载影响,同时获取了比传统粗粒度模型更佳的预测准确率.在实际应用中,该模型不需要分析记录复杂的磁盘内部活动细节,也不需要繁杂的参数采集,仅需要存储系统中宏观的统计量作为参数,且预测精度与细粒度模型近似.通过实验验证,该模型在能耗预测上的平均误差为3%,并且针对同步IO及异步IO都有较好的预测效果.此外,该模型还可以应用于各种在线系统的能耗预测.

关键词: 存储系统, 能耗, 建模, 磁盘, 电源管理

Abstract: Due to the rapid development of big data in the data center, power consumption of storage system is a major issue in today’s datacenters. How to reduce the power consumption of storage systems has become an urgent issue and a hot research topic in the field of computer science. As the hard disk drive is the primary storage medium in today’s storage systems, modeling hard-disk power consumption is attracting more attention in the current state of research. The accurate power consumption model of disk can not only solve the problem of power matching in data center devices, but also estimate the accuracy of energy-efficient solutions. We develop a statistic-based hard-disk power modeling method that estimates the power consumption of storage workloads. The model makes up the weakness of traditional fine-grained model and it is more accurate than the coarse-grained model. In practical applications, it does not need to record the disk internal activities, and does not need to trace complex parameter. Our power estimation results are highly accurate, which means error of 3% and the model is applicable to the synchronous IO and asynchronous IO. Moreover, our model can also be applied to various online storage systems and data center.

Key words: storage system, energy conservation, modeling, hard-disk, power management

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