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

被引：0
|
作者：
Zhang X.-D. [1 ,2 ]
Xia X.-J. [1 ]
Lyu H.-F. [1 ,2 ]
Gong X.-C. [3 ]
Lian M.-J. [1 ,2 ]
机构：
[1] Shenyang Institute of Computing Technology, Chinese Academy of Science, Shenyang
[2] University of Chinese Academy of Sciences, Beijing
[3] China University of Petroleum (East China), Qingdao
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 01期
关键词：
Big data; Computer application; Dynamic; Load balancing; Network parallel computing; Physiological data flow;
D O I：
10.13229/j.cnki.jdxbgxb20181250
中图分类号：
学科分类号：
摘要：
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. © 2020, Jilin University Press. All right reserved.
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页码：247 / 254
页数：7
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