Peer-to-Peer Networking and Applications

, Volume 8, Issue 3, pp 493–500 | Cite as

Pre-allocation based flash crowd mitigation algorithm for large-scale content delivery system

  • Dan HuangEmail author
  • Min Zhang
  • Yi Zheng
  • Changjia Chen
  • Yan Huang


A burst of requests may starve the upload capacity of server and degrade the Quality of Service (QoS) when a popular or time-critical content is released in content delivery system. With an enormous volume of popular content available in the network, as well as the growing size of online users, the previous improvement of download protocol is far from enough to handle the flash crowd in large-scale system. In this paper, a novel flash crowd mitigation algorithm called Peer Assisted Pre-allocation (PAPA) is proposed from a new perspective, in which “proactive” strategies are provided to handle sudden workload of the system. Through numerical simulation and practical experiment in download platform of Tencent, it is proved that benefited from the collaboration of servers and helpers, PAPA is able to save the peak server bandwidth by over 30 % and thus greatly reduce the operating costs of system.


Content delivery Flash crowd Pre-allocation Fluid model 



This work was supported in part by the National Science Foundation of China under Grant No. 61271199 and the Fundamental Research Funds in Beijing Jiaotong University under Grant No. W11JB00630.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Dan Huang
    • 1
    Email author
  • Min Zhang
    • 2
  • Yi Zheng
    • 3
  • Changjia Chen
    • 4
  • Yan Huang
    • 5
  1. 1.Network and Information Security Research DepartmentElectronic Technology Information Research Institute, MIITBeijingChina
  2. 2.Technology Innovation CenterChina TelecomBeijingChina
  3. 3.Network Technology Research InstituteChina United Network Communications Corporation LimitedBeijingChina
  4. 4.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  5. 5.Tencent ResearchShanghaiChina

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