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基于云端协同计算架构的边缘端I/O密集型虚拟机资源分配方案 被引量:6

Edge-side I/O-intensive virtual machine resource allocation scheme based on cloud and edge collaborative computing architecture
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摘要 大型制造业生产车间业务流程复杂,传统固定资源配置方式将产生负载不均衡的问题。针对此问题,设计并实现了一种基于云端协同架构的边缘端I/O密集型虚拟机资源的分配算法,通过定义计算节点中每个I/O密集型虚拟机三个维度的信息计算得出I/O密集型虚拟机的优先级,并按最大优先级队列实时统一分配硬件资源。实验结果表明,该算法在应对工业机械设备高响应和高通量的要求上有较为明显的提升,为实际生产起到了优化资源配置的作用。 The business process of large-scale manufacturing workshops is complex,and the traditional fixed resource allocation method will cause unbalanced load. Aiming at this problem,this paper designed and implemented an edge-side I/O-intensive virtual machine resource allocation algorithm based on cloud collaborative architecture. By defining the three dimensions of each I/O-intensive virtual machine in the compute node,it calculated the priority of the I/O-intensive virtual machine,and uniformly allocated the hardware resources in real-time according to the maximum priority list. The experimental results show that the algorithm has a significant improvement in responding to the requirements of high response and high throughput of industrial machinery equipment,and plays an important role in optimizing resource allocation for actual production.
作者 赵龙乾 满君丰 彭成 薛振泽 Zhao Longqian;Man Junfeng;Peng Cheng;Xue Zhenze(School of Computer Science,Hunan University of Technology,Zhuzhou Hunan 412007,China;School of Automation,Central South University,Changsha 410083,China)
出处 《计算机应用研究》 CSCD 北大核心 2020年第9期2734-2738,共5页 Application Research of Computers
基金 国家自然科学基金资助项目(61871432) 湖南省自然科学基金资助项目(2018JJ4063,2019JJ60008,2017JJ3065) 湖南省教育厅重点项目(16A059,17A052) 湖南省研究生创新基金资助项目(CX2018B740)。
关键词 云端协同 I/O密集型 三维度信息 资源分配 cloud and edge collaboration I/O-intensive three dimensional information resource allocation
作者简介 赵龙乾(1995-),男,安徽合肥人,硕士研究生,主要研究方向为工业大数据边缘计算、云计算;满君丰(1976-),男,教授,博士,主要研究方向为网络化软件、大数据分析;通信作者:彭成(1982-),男,副教授,博士,主要研究方向为工业大数据分析(chengpeng@csu.edu.cn);薛振泽(1997-),男,硕士研究生,主要研究方向为大数据分析.
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  • 1刘祥武.用示功图计算动液面深度的研究方法[J].中国石油和化工标准与质量,2012,32(16):90-90. 被引量:3
  • 2李国杰.大数据研究的科学价值[J].中国计算机学会通讯,2012,8(9):8-15.
  • 3Dean J, Sanjay G. MapReduce: simplified data processing on largeclusters [J]. Communications of the ACM, 2008,51 (1) :107-113.
  • 4Zaharia M, Chowdhury M,Das T, et al. Resilient distributed data-sets: a fault-tolerant abstraction for in-memory cluster computing[C ] //Proc of the 9th USENIX Conference on Networked Systems De-sign and Implementation. [S. 1. ] :USENIX Association, 2012.
  • 5Toshniwal A, TanejaS,ShuklaA,et al. Storm@ Twitter[ C]//Procof ACM SIGMOD International Conference on Management of Data.New York:ACM Press,2014: 147-156.
  • 6Malewicz G,Austem M H,Bik A J C, et al. Pregel: a system forlarge-scale graph processing [ C ] //Proc of ACM SIGMOD Internation-al Conference on Management of Data. New York: ACM Press,2010:135-146.
  • 7Reiss C, Tumanov A, Ganger G R, ei al. Heterogeneity and dyna-micity of clouds at scale: Google trace analysis[ C]//Proc of the 3rdACM Symposium on Cloud Computing. New York:ACM Press,2012.
  • 8孙矣.面向云环境数据中心的高效资源调度机制研究[D].北京:北京邮电大学,2012.
  • 9Sharma B, Chudnovsky V, Hellerstein J L, et al. Modeling and syn- thesizing task placement constraints in Google compute clusters [ C ]// Proc of the 2rid ACM Symposium on Cloud Computing. New York: ACM Press,2011.
  • 10Vavilapalli V K, Murthy A C, Douglas C, et al. Apache HadoopYam:yet another resource negotiator [ C ]//Proc of the 4th AnnualSymposium on Cloud Computing. New York: ACM Press,2013.

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