AI & eBPF based performance anomaly detection system

被引：7

作者：

Ben-Yair, Ido ^{[1
]}

Rogovoy, Pavel ^{[2
]}

Zaidenberg, Nezer ^{[2
]}

机构：

[1] Open Univ Israel, Raanana, Israel

[2] Coll Management, Rishon Leziyyon, Israel

来源：

SYSTOR '19: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL SYSTEMS AND STORAGE CONFERENCE | 2019年

关键词：

eBPF; Anomaly Detection; Performance;

D O I：

10.1145/3319647.3325842

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We describe means to run eBPF on a production environment for systems inspection. We examine the inspected system outputs in order to train and generate a model for the host. We model the specific application and network traffic usage on the site based on the data collected by eBPF. Our system generates alerts when an anomaly in performance is detected on a specific host. These warnings can be used to discover the root cause for performance problems, cyber-security issues and warn in advance about potential performance peaks.

引用

页码：180 / 180

页数：1

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