计算机科学 ›› 2013, Vol. 40 ›› Issue (4): 59-63.

• 2012多值逻辑专栏 • 上一篇    下一篇

机群系统中空闲结点的功耗管理

刘勇鹏,卢凯,迟万庆   

  1. 国防科学技术大学计算机学院长沙410073;国防科学技术大学计算机学院长沙410073;国防科学技术大学计算机学院长沙410073
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家863重大项目(2012AA01A301),国家自然科学基金(60903059,1)资助

Power Management of Idle Nodes in Clusters

LIU Yong-peng,LU Kai and CHI Wan-qing   

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对机群系统中存在的大量空闲活跃结点所造成的严重能耗浪费,提出空闲结点的cache 式动态功耗管理模型,即利用结点多级休眠机制,将空闲结点划分为不同休眠等级的结点集合,每级休眠状态对应一级结点储备cache,力求获得近似活跃状态的系统响应速率,以及近似最深休眠状态的能耗节省。基于cache式功耗管理模型,综合能耗与响应速率两个因素,设计了空闲结点在不同休眠状态之间的动态升降级算法、基于储备池的资源结点分配与回收算法以及储备额阈值自适应算法,以在保证系统响应速率的同时降低系统能耗。实验表明,提出的空闲结点cache式功耗管理技术在作业相对延迟仅增加0.99%的代价下,系统空闲结点功耗降低69.51%,优化效果显著。

关键词: 计算机群,功耗管理,结点休眠

Abstract: Existence of massive active idle nodes causes huge energy waste in large scale systems.Cache-style power management for idle nodes was proposed to schedule the power states of idle nodes.According to their different sleep states,idle nodes are placed into multiple groups with corresponding sleep states.It is expected to achieve a system response speed similar to the active state and a power saving similar to the deepest sleep state.The idle nodes are dynamically transformed between different sleep groups.Assuring response speed of system,idle node is put into a sleep state as deep as possible.In our experiments,CPMI conserves the power consumption of idle nodes by 69.51% with the cost of relative slowdown only by 0.99%.

Key words: Compute cluster,Power management,Node sleep

[1] Feng Wu-chun.Making a case for efficient supercomputing [J].ACM Queue,2003,1(7):54-64
[2] Top 500.Top500List .http://www.top500.org,2012
[3] U.S.Environmental Protection Agency.Report to congress on server and data center energy efficiency .http://www.energy star.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress _Final1.pdf,2007
[4] Krioukov A,Mohan P,Alspaugh S,et al.NapSAC:Design and implementation of a power-proportional web cluster [J].ACM SIGCOMM Computer Communication Review,2011,41(1):102-108
[5] Mustafa R M,Nishkam R,Srihari C,et al.Power management for heterogeneous clusters:an experimental study[C]∥Procee-dings of the 2nd International Green Computing Conference (IGCC’11).Orlando,Florida,2011:1-8
[6] Liu Yong-peng,Zhu Hong.A survey of the research on power management techniques for high performance systems [J].Software Practice and Experience,2010,40(1):943-964
[7] Chase J,Aderson D,Thakar P,et al.Managing energy and server resources in hosting centers[C]∥Proceedings of the 18th ACM Symposium on Operating Systems Principles (SOSP’01).Banff,Canada,2001:103-116
[8] Hong Liang-jie,Davison B D.Empirical study of topic modeling in Twitter[C]∥Proceedings of the First Workshop on Social Media Analytics.Washington DC,USA,2010:80-88
[9] Rosen-Zvi M,Griffiths T,Steyvers M,et al.The author-topic model for authors and documents[C]∥Proceedings of the 20th conference on Uncertainty in artificial intelligence.AUAI Press Arlington,Virginia,United States,2004:487-494
[10] Steyvers M,Smyth P,Rosen-Zvi M,et al.Probabilistic author-topic models for information discovery[C]∥Proceedings of the Tenth ACM SIGKDD International Confe-rence on Knowledge Discovery and Data Mining.Seattle,WA,USA,2004:306-315
[11] Ramage D,Dumais S,Liebling D.Characterizing micorblogs with topic models[C]∥Proceedings of the 4th International Confe-rence on Weblogs and Social Media.Washington DC,U S A,2010
[12] Daud A,Li Juan-zi,Zhou Li-zhu,et al.Exploiting temporal authors interests via temporal-author-topic modeling[C]∥Proceedings of 5th International Conference on Advanced Data Mi-ning and Applications.Verlag Berlin,Heidelberg,2009:435-443
[13] Liu Yan,Niculescu-Mizil A,Gryc W.Topic-link LDA:jointmodels of topic and author community[C]∥Proceedings of the 26th Annual International Conference on Machine Learning.Montreal,QC,Canada,2009:665-672
[14] Wang Xue-rui,McCallum A.Topics over time:a non-markovcontinuous-time model of topical trends[C]∥Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Philadelphia,USA,2006:424-433
[15] McCallum A,Corrada-Emmanuel A,Wang Xue-rui.Topic androle discovery in social networks[C]∥Proceedings of 19th International Joint Conference on Artificial Intelligence,Morgan Kaufmann Publishers Inc.San Francisco,CA,USA:786-791
[16] Mei Qiao-zhu,Liu Chao,Su Hang,et al.A probabilistic ap-proach to spatiotemporal theme pattern mining on weblogs[C]∥Proceedings of the 15th International Conference on Word Wide Web.Edinburgh,Scotland,UK,2006:533-542
[17] Su Yi-zhou,Han Jia-wei,Gao Jing,et al.iTopicModel:Information Network-Integrated Topic Modeling[C]∥Proceeding of the 9th IEEE International Conference on Data Mining.Miami,USA,2009:487-497
[18] Mei Qiao-zhu,Cai Deng,Zhang Duo,et al.Topic modeling with network regularization[C]∥Proceeding of the 17th InternationalWorld Wide Web Conference.Beijing,China,2008:101-111

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