Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 11-22.doi: 10.11896/jsjkx.210900117

• Novel Distributed Computing Technology and System • Previous Articles     Next Articles

Reducing Head-of-Line Blocking on Network in Hadoop Clusters

TIAN Bing-chuan, TIAN Chen, ZHOU Yu-hang, CHEN Gui-hai, DOU Wan-chun   

  1. Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China
  • Received:2021-09-14 Revised:2021-12-12 Online:2022-03-15 Published:2022-03-15
  • About author:TIAN Bing-chuan,born in 1993,Ph.D.His main research interests include network verification,programmable networks and distributed systems.
    TIAN Chen,born in 1978,Ph.D,asso-ciate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include data center networks,distributed systems,internet streaming and urban computing.
  • Supported by:
    Key-Area Research and Development Program of Guangdong Province(2020B0101390001) and National Natural Science Foundation of China(61772265,61802172,62072228).

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Abstract: Users of big data analytics systems want the execution time of tasks to be as short as possible.However,during task execution,both network and computational moments may become resource bottlenecks that hinder task execution.Through the observation and analysis of the big data analysis system,the following conclusions are drawn:1)the data-parallel framework should switch between multiple working modes depending on the current resource bottlenecks;2)the scheduling of subtasks should fully consider the new tasks that may arrive in the future,not only the currently submitted tasks.Based on the above observations,a new task scheduling system Duopoly is designed and implemented,which consists of two parts:cans,a network scheduler that senses computational resources,and nats,a sub-task scheduler that senses network resources.The effectiveness of Duopoly is evaluated by small-scale physical clusters and large-scale simulation experiments,and the experimental results show that Duopoly can reduce the average task completion time by 37.30%~76.16% compared with existing work.

Key words: Hadoop cluster, Head-of-line blocking, Job scheduling, Network scheduling

CLC Number: 

  • TP393
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