Haiwen Chen1, Guang Yu1, Fang Liu2, Zhiping Cai1, *, Anfeng Liu3, Shuhui Chen1, Hongbin Huang1, Chak Fong Cheang4
CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981
Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage,
network usage, business monitoring data) will be generated every day. How to closely
monitor various KPIs, and then quickly and accurately detect anomalies in such huge data
for troubleshooting and recovering business is a great challenge, especially for unlabeled
data. The generated KPIs can be detected by supervised learning with labeled data, but
the current problem is that most KPIs are unlabeled. That is a time-consuming and
laborious work to label anomaly for company engineers. Build an unsupervised model to
detect unlabeled More >
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