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容器云多维资源利用率均衡调度研究 被引量:18

Container cloud multi-dimensional resource utilization balanced scheduling
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摘要 在OpenShift容器云平台上针对其调度策略进行研究和改进,提出了基于多维资源空闲率权重的评价函数和调度方法。该方法综合考虑物理节点CPU、内存、磁盘、网络带宽空闲率和已部署的容器应用个数等因素,利用模糊层次分析法(fuzzy analytic hierarchy process,FAHP)自动建模求解容器应用多维资源权重参数。实验表明,新的调度方案能够使集群多维资源利用率更加均衡,从而提升资源的利用率和集群性能。 This paper researched and improved the scheduling policy on the OpenShift container cloud platform and proposed the evaluation function and scheduling method based on the multi-dimensional resource idle rate weight.This scheme consi-dered the factors of CPU,memory,disk,bandwidth and the deployed container applications on the physical machines,and used the FAHP(fuzzy analytic hierarchy process)to automatically model and solve the multi-dimensional resource weighting parameters of the container applications.Experiments show that the new scheduling enables the cluster multi-dimensional resources utilization more balanced,improves resource utilization and cluster performance.
作者 龚坤 武永卫 陈康 Gong Kun;Wu Yongwei;Chen Kang(Dept.of Computer Science&Technology,Tsinghua University,Beijing 100084,China)
出处 《计算机应用研究》 CSCD 北大核心 2020年第4期1102-1106,共5页 Application Research of Computers
基金 国家重点研发计划资助项目(2016YFB1000504) 国家自然科学基金资助项目(61433008,61373145,61572280,U1435216) 中国博士后科学基金资助项目(2018M630162)。
关键词 容器云 调度策略 OpenShift平台 评价函数 负载均衡 container cloud scheduling strategy OpenShift platform evaluation function load balancing
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  • 1王振,刘茂.应用区间层次分析法(IAHP)研究高层建筑火灾安全因素[J].安全与环境学报,2006,6(1):12-15. 被引量:64
  • 2Buyya R, Yeo C S, Venugopal S, et al. Cloud computing and e-merging IT platforms: vision, hype, and reality for delivering computing as the 5th utility[J]. Future Generation Computer Systems,2009,25(6) :599-616.
  • 3Armbrust M, Fox A, Griffith R, et al. Above the Clouds: A Berkeley View of Cloud Computing [EB/OL]. http..//www, ee- cs. berkeley, edu/Pubs/TechRpts/2009/EECS-2009-28, html, February 2009.
  • 4Lin Wei-wei, Qi De-yu. Research on Resource Self-Organizing Model for Cloud Computing[C]// 2010 International Conference on Internet Technology and Applications. 2010:1-5.
  • 5Von L G, Wang L, Younge A J, et al. Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters[C] ff Proc. of IEEE International Conference on Cluster Computing 2009. New Orleans, LA, USA, 2009 : 1-10.
  • 6Ge R, Feng X, Cameron K. Performance-constrained distributed dvs scheduling for scientific applications on power-aware clus ters[C]//Proceedings of the 2005 ACM/IEEE Conference on Supercomputing. IEEE Computer Society, Washington DC, USA, 2005 : 34.
  • 7Venkatachalam V, Franz M. Power reduction techniques for mi- croprocessor systems[J]. ACM Computing Surveys (CSUR), 2005,37(3) : 195-237.
  • 8Mezmaz M, MelabN, KessaciY, etal. Aparallel bi-objective hy- brid metaheuristic for energy-aware scheduling for cloud compu- ting systems[J]. Journal of Parallel and Distributed Computing (JPDC), 2011,71(11) : 1497-1508.
  • 9Lee Y C,Zomaya A Y. A novel state transitionmethod formeta- heuristic-based scheduling in heterogeneous computing systems [J]. IEEE Transactions on Parallel and Distributed Systems, 2008,19(9) : 1215-1223.
  • 10Beloglazov A, Abawajy J, Buyya R. Energy-Aware Resource Al- location Heuristics for Efficient Management of Data Centers for Cloud Computing[J]. Future Generation Computer Systems, 2012,28(5) : 755-768.

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