Computer Science ›› 2020, Vol. 47 ›› Issue (5): 277-283.doi: 10.11896/jsjkx.190600048

• Computer Network • Previous Articles     Next Articles

Edge Computing-oriented Storm Edge Node Scheduling Optimization Method

JIAN Cheng-feng, PING Jing, ZHANG Mei-yu   

  1. College of Computer Science & Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2019-06-11 Online:2020-05-15 Published:2020-05-19
  • About author:JIAN Cheng-feng,born in 1973,Ph.D,postgraduate,associate professor,is a member of China Computer Federation.His main research inte-rests include cloud computing,CAD and image processing.
    ZHANG Mei-yu,born in 1965,M.D,professor,is a member of China Compu-ter Federation.Her main research inte-rests include data mining and image processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61672461,61672463).

Abstract: Edge computing has the demands of high real-time and big data interactive processing.The key problems of edge computing performance are long scheduling,high communication latency and unbalanced load among the edge heterogeneous nodes.Traditional cloud computing platforms are difficult to meet these new requirements.This paper focuses on the scheduling optimization method of Storm edge nodes in the edge computing environment.Firstly,a Storm task offloads scheduling model for edge computing is established.And then a heuristic dynamic programming algorithm is put forward to realize real-time dynamic allocation of topological tasks among edge heterogeneous nodes.By changing the sorting method of the Task instance and the mapping relationship between the Task instance and the Slot,the global optimization scheduling is achieved.Aiming at the problem that the concurrency of topological tasks may be greater than the maximum depth of the JVM stack,a scheduling strategy based on bat algorithm is put forward,the global scheduling scheme is calculated according to the information of the topology task and the CPU information of the edge node.Experiments show that compared with the current Storm scheduling algorithm,the proposed algorithm has an average increase of about 60% in the CPU utilization metrics of the edge node and an average increase of about 8.2% in the throughput metrics of the cluster.Therefore,the proposed algorithm can meet the high real-time processing requirements between edge nodes better.

Key words: Bat algorithm, Dynamic planning, Edge computing, Scheduling, Storm

CLC Number: 

  • TP391
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