Job Performance Optimization Method Based on Data Balance in the Wireless Sensor Networks

International journal of online engineering

Guozeng Zhao¹  Ziyu Sun¹  Meng Li¹  Zhiguo Lv² 

[1] School of Computer Science and Engineering, Luoyang Institute of Science and Technology;State Key Laboratory of Integrated Services Networks, Xidian University

关键词: wireless sensor networks;    MapReduce;    hash function;    data skew;    heterogeneity-aware;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: International Association of Online Engineering

【 摘 要 】

In the wireless sensor network, the representative MapReduce computing model based on data center has been widely used in large-scale data processing. In the data transmission phase, the wireless sensor network system uses the hash method to distribute data for each Reduce task based on the number of Reduce tasks. This data partitioning method based on the hash function results in non-uniform distribution of the output data in the data transmission phase and further leads to skewing of the input data in the Reduce task. Data skew will result in load imbalance in the Reduce phase and causes the system performance to degrade. In order to eliminate the data skew problem in the Reduce phase, this paper presents a load balancing method, which consists of two parts: the virtual partitioning method based on the consistent hashing and the heterogeneity-aware loads balancing (HLB) algorithm. The experimental results show that the proposed method can eliminate the data skew in the Reduce phase and distribute the load equitably for each Reduce task. In addition, the method produces less system overhead.

【 授权许可】

Unknown