Open AccessResearch on Cluster Monitoring and Prediction Platform based on Zabbix Technology
Author(s) -
Peng Cong,
Bian Shenghua,
Hongliang Zheng,
Tang Baoyu
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/512/1/012155
Subject(s) - cluster (spacecraft) , computer science , reliability (semiconductor) , real time computing , function (biology) , stability (learning theory) , term (time) , data mining , operating system , machine learning , power (physics) , physics , quantum mechanics , evolutionary biology , biology
With the rapid development of Internet and computer technology, cluster technology has been widely applied to the explosive growth of network data. With the successful construction of the cluster and the continuous expansion of nodes, the reliability and stability of the cluster itself has become an important factor that cannot be ignored, so various monitoring systems emerge. Based on the above reasons, this paper proposes a real-time monitoring and prediction platform for enterprise users based on Zabbix. The monitoring and prediction platform monitors all kinds of indexes in the cluster in real time and predicts important performance indexes in real time. When monitoring or predicting the abnormal situation, the system can warn the users, which is conducive to the real-time understanding of the cluster operation. The short-term real-time prediction model of cluster monitoring index is introduced in detail in this paper. Finally, the real-time monitoring function of indicators is designed and analyzed based on the prediction model.