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[Author] Jing JIANG(3hit)

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  • Improving the Incast Performance of Datacenter TCP by Using Rate-Based Congestion Control

    Jingyuan WANG  Yunjing JIANG  Chao LI  Yuanxin OUYANG  Zhang XIONG  

     
    LETTER-Communications Environment and Ethics

      Vol:
    E97-A No:7
      Page(s):
    1654-1658

    We analyze the defects of window-based TCP algorithm in datacenter networks and propose Rate-based Datacenter TCP (RDT) algorithm in this paper. The RDT algorithm combines rate-based congestion control technology with ECN (Explicit Congestion Notification) mechanism of DCTCP. The experiments in NS2 show that RDT has a potential to completely avoid TCP incast collapse in datacenters and inherit the low latency advantages of DCTCP.

  • General Constructions for (υ,4,1) Optical Orthogonal Codes via Perfect Difference Families

    Jing JIANG  Dianhua WU  Pingzhi FAN  

     
    LETTER-Sequences

      Vol:
    E95-A No:11
      Page(s):
    1921-1925

    Optical orthogonal codes (OOCs) were introduced by Salehi, as signature sequences to facilitate multiple access in optical fibre networks. The existence of optimal (υ,3,1)-OOCs had been solved completely. Although there are some partial results, the existence of optimal (υ, 4, 1)-OOCs is far from settled. In this letter, three general constructions for (υ, 4, 1)-OOCs via perfect difference families are presented, new infinite classes of (υ, 4, 1)-OOCs are then obtained.

  • Inferring User Consumption Preferences from Social Media

    Yang LI  Jing JIANG  Ting LIU  

     
    PAPER-Artificial Intelligence, Data Mining

      Pubricized:
    2016/12/09
      Vol:
    E100-D No:3
      Page(s):
    537-545

    Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.