• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Hu Zhiyao, Li Dongsheng, Li Ziyang. Recent Advances in Datacenter Flow Scheduling[J]. Journal of Computer Research and Development, 2018, 55(9): 1920-1930. DOI: 10.7544/issn1000-1239.2018.20180156
Citation: Hu Zhiyao, Li Dongsheng, Li Ziyang. Recent Advances in Datacenter Flow Scheduling[J]. Journal of Computer Research and Development, 2018, 55(9): 1920-1930. DOI: 10.7544/issn1000-1239.2018.20180156

Recent Advances in Datacenter Flow Scheduling

More Information
  • Published Date: August 31, 2018
  • Flow scheduling techniques impose an important impact on the performance of the data center. Flow scheduling techniques aim at optimizing the user experience by controlling and scheduling the transmission link, priority and transmission rate of data flows. Flow scheduling techniques can achieve various optimization objects such as reducing the average or weighted flow completion time, decreasing the delay of long-tail flows, optimizing the transmission of flows with deadline constraints, improving the utilization of the network link. In this paper, we mainly review the recent research involving flow scheduling techniques. First, we briefly introduce data center and flow scheduling problem and challenges. These challenges mainly lie in the means to implement flow scheduling on network devices or terminal hosts, and how to design low-overhead highly-efficient scheduling algorithms. Especially, the coflow scheduling problem is proved NP-Hard to solve. Then, we review the latest progress of flow scheduling techniques from two aspects, i.e., single-flow scheduling and coflow scheduling. The divergence between single-flow scheduling techniques and coflow scheduling techniques is the flow relationship under different applications like Web search and big data analytics. In the end of the paper, we outlook the future development direction and point out some unsolved problems involving flow scheduling.
  • Related Articles

    [1]Zheng Zhi, Xu Tong, Qin Chuan, Liao Xiangwen, Zheng Yi, Liu Tongzhu, Tong Guixian. Multi-Source Contextual Collaborative Recommendation for Medicine[J]. Journal of Computer Research and Development, 2020, 57(8): 1741-1754. DOI: 10.7544/issn1000-1239.2020.20200149
    [2]Yu Yaxin, Liu Meng, Zhang Hongyu. Research on User Behavior Understanding and Personalized Service Recommendation Algorithm in Twitter Social Networks[J]. Journal of Computer Research and Development, 2020, 57(7): 1369-1380. DOI: 10.7544/issn1000-1239.2020.20190158
    [3]Du Yumeng, Zhang Weinan, Liu Ting. Topic Augmented Convolutional Neural Network for User Interest Recognition[J]. Journal of Computer Research and Development, 2018, 55(1): 188-197. DOI: 10.7544/issn1000-1239.2018.20160892
    [4]Pan Xiaoyan, Lou Zhengzheng, Ji Bo, Ye Yangdong. Interpretable Clustering with Multi-View Generative Model[J]. Journal of Computer Research and Development, 2017, 54(8): 1713-1723. DOI: 10.7544/issn1000-1239.2017.20170175
    [5]Li Quangang, Liu Qiao, Qin Zhiguang. Modeling and Simulation of Communication Network Based on Topic Model[J]. Journal of Computer Research and Development, 2016, 53(1): 206-215. DOI: 10.7544/issn1000-1239.2016.20148120
    [6]Peng Min, Huang Jiajia, Zhu Jiahui, Huang Jimin, Liu Jiping. Mass of Short Texts Clustering and Topic Extraction Based on Frequent Itemsets[J]. Journal of Computer Research and Development, 2015, 52(9): 1941-1953. DOI: 10.7544/issn1000-1239.2015.20140533
    [7]Tan Wentang, Wang Zhenwen, Yin Fengjing, Ge Bin, and Xiao Weidong. A Partial Comparative Cross Collections LDA Model[J]. Journal of Computer Research and Development, 2013, 50(9): 1943-1953.
    [8]Han Xiaohui, Ma Jun, Shao Haimin, and Xue Ran. An LDA Based Approach to Detect the Low-Quality Reply Posts in Web Forums[J]. Journal of Computer Research and Development, 2012, 49(9): 1937-1946.
    [9]Wu Youzheng, Zhao Jun and Xu Bo. Sentence Retrieval with a Topic-Based Language Model[J]. Journal of Computer Research and Development, 2007, 44(2): 288-295.
    [10]Duan Jiangjiao, Xue Yongsheng, Lin Ziyu, Wang Wei, Shi Baile. A Novel Hidden Markov Model-Based Hierarchical Time-Series Clustering Algorithm[J]. Journal of Computer Research and Development, 2006, 43(1): 61-67.
  • Cited by

    Periodical cited type(3)

    1. 王粲,邹伟东,夏元清. 基于衰减正则化项的I-ELM智能制造动态调度. 人工智能. 2023(01): 17-28 .
    2. 王一宾,缪佳李,程玉胜. 信息适应性分层粒化的多标签特征选择. 安庆师范大学学报(自然科学版). 2022(04): 37-43 .
    3. 曹天成,姚丽莎,陶朗. 隶属度阈值函数极限学习机的多标签学习. 怀化学院学报. 2022(05): 59-67 .

    Other cited types(8)

Catalog

    Article views (1861) PDF downloads (825) Cited by(11)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return