Research Article
Online Monitoring Method of Big Data Load Anomaly Based on Deep Learning
@INPROCEEDINGS{10.1007/978-3-030-67874-6_42, author={Cao-Fang Long and Heng Xiao}, title={Online Monitoring Method of Big Data Load Anomaly Based on Deep Learning}, proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2021}, month={1}, keywords={Deep learning Network behavior big data Online monitoring}, doi={10.1007/978-3-030-67874-6_42} }- Cao-Fang Long
Heng Xiao
Year: 2021
Online Monitoring Method of Big Data Load Anomaly Based on Deep Learning
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-67874-6_42
Abstract
In the process of monitoring the abnormal load of big data in network behavior, more network traffic resources are consumed, which leads to the low efficiency of its operation. Therefore, an on-line monitoring method for the abnormal load of big data in network behavior based on deep learning is proposed. The online monitoring model of load anomaly is established, the network data distribution is analyzed, and the adaptive random link configuration is adopted to improve the channel balance and the positioning ability of the abnormal load. The load anomaly is identified through the load pattern and the online monitoring is completed. The experimental results show that the proposed method consumes about 50% of the traffic of the traditional method, which can effectively reduce the traffic consumption and improve the utilization rate of network resources. This method is more suitable for online monitoring of big data load anomalies in network behavior.
