Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 247-254.doi: 10.13229/j.cnki.jdxbgxb20181250

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Dynamic load balancing of physiological data flow in big data network parallel computing environment

Xiao-dong ZHANG1,2(),Xiao-jun XIA1,Hai-feng LYU1,2,Xu-chao GONG3,Meng-jia LIAN1,2   

  1. 1. Shenyang Institute of Computing Technology, Chinese Academy of Science, Shenyang 110168, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. China University of Petroleum (East China), Qingdao 266580, China
  • Received:2018-12-19 Online:2020-01-01 Published:2020-02-06

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Abstract:

In the medical big data service system, there exists the problem of the unbalanced dynamic load of physiological data flow. The processing power of the traditional method is limited to the window range that can be processed by the node where the operator is located. In the state where the data is gradually increased, the processing capacity is insufficient, the data flow congestion is easy to occur, and the load distribution of the whole system is neglected. To solve these problems, a new dynamic load balancing method for physiological data flow based on network parallel computing environment is proposed in this paper. Firstly, the Hash value of the tuple key is used to obtain the corresponding data block of the node, and the corresponding target node is obtained by using the data block record, and the data tuple is output. At the same time, the entropy of parallel computing is extended to define the heterogeneous cluster and solve it. Then, the parallel computing entropy in the network parallel computing environment is regarded as the measurement index of the dynamic load balancing of physiological data flow in the medical big data service system. Finally, by judging whether load migration is necessary by parallel computing entropy, the way and amount of migration tasks are determined by parallel computing entropy, so we can make migration decision and realize dynamic load balancing of physiological data flow in parallel environment of large data network. The experimental results show that the proposed method is highly feasible, and the calculation performance and dynamic load balance are good.

Key words: computer application, big data, network parallel computing, physiological data flow, dynamic, load balancing

CLC Number: 

  • TP399

Table 1

Comparison of calculation performance of three methods"

核数 本文方法 文献[5]方法 文献[6]方法
计算时间/s 加速比 并行效率/% 计算时间/s 加速比 并行效率/% 计算时间/s 加速比 并行效率/%
200 729 1 100 851 1 100 822 1 100
400 395 2.05 96.35 902 0.81 49.21 769 1.08 62.13
600 286 2.71 86.21 953 0.69 24.96 751 1.15 42.3
800 161 2.93 73.05 1 025 0.58 17.21 722 1.31 21.35

Fig.1

Load balancing response spectrum before and after processing by proposed method"

Fig.2

Load change of neighbor nodes by proposed method"

Fig.3

Comparison of average flow of maximum load nodes by three methods"

Fig.4

Changes in load balance by using three methods"

Fig.5

Graph for partial data of experimental data flow"

Fig.6

Time?consuming comparison of three load balancing methods"

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