李维虎,张顶山,崔慧明,周龙,朱志挺,谢挺.数据中心网络coflow调度机制结构构建及仿真[J].电子测量技术,2019,42(10):78-81
数据中心网络coflow调度机制结构构建及仿真
Data center network coflow scheduling mechanism structure construction and simulation
  
DOI:
中文关键词:  数据中心  网络  语义相关流  调度机制  性能
英文关键词:data centres  network  semantic correlation flow  scheduling mechanism  performance
基金项目:
作者单位
李维虎 国网白银供电公司 白银 730900 
张顶山 国网白银供电公司 白银 730900 
崔慧明 国网白银供电公司 白银 730900 
周龙 国网白银供电公司 白银 730900 
朱志挺 国网白银供电公司 白银 730900 
谢挺 国家数字交换系统工程技术研究中心 郑州 450002 
AuthorInstitution
Li Weihu State Grid Silver Power Supply Company, Baiyin 730900, China 
Zhang Dingshan State Grid Silver Power Supply Company, Baiyin 730900, China 
Cui Huiming State Grid Silver Power Supply Company, Baiyin 730900, China 
Zhou long State Grid Silver Power Supply Company, Baiyin 730900, China 
Zhu Zhiting State Grid Silver Power Supply Company, Baiyin 730900, China 
Xie Ting ational Digital Exchange System Engineering Technology Research Center, Zhengzhou 450002, China 
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中文摘要:
      重新构建得到了一种coflow调度算法-DeepCS,将coflow资源视图看成是需要进行后续处理的图像,根据之前学习策略来达到coflow的最佳调度效果。利用DNN提取特征参数时不必通过人为手动的方法进行设计,通过单独学习过程便可实现,给出深度增强学习系统。训练输入包含了各项网络与任务情景,并以动作概率分布作为输出,EPiSOdE作为单位开展训练过程。仿真结果得到:当coflow到达速率变大后,将会导致所有算法需要更长的coflow完成时间,此时调度算法流时间与的工作压力都会增加,从而形成更长的coflow平均完成时间;在较低的coflow到达速率下,VARYS和DeepCS具有相似的性能,都比PFABRiC的性能更好,并且DeepCS性能提升最快。
英文摘要:
      DeepCS, a kind of coflow scheduling algorithm, is obtained through reconstruction. The coflow resource view is regarded as the image to be processed later, and the optimal scheduling effect of coflow is achieved according to the previous learning strategy.The feature parameters extracted by DNN need not be designed by manual method, and can be realized by separate learning process.The training input includes various network and task situations, with the motion probability distribution as the output, and EPiSOdE as the unit to carry out the training process.The simulation results are as follows: when the coflow reaches a larger rate, all algorithms will need longer coflow completion time. At this point, the flow time and working pressure of the scheduling algorithm will increase, thus forming a longer average coflow completion time.Under the lower coflow arrival rate, VARYS and DeepCS have similar performance, both of which are better than PFABRiC, and DeepCS has the fastest performance improvement.
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