Anomaly Detection and Diagnosis for Container-based Microservices with Performance Monitoring

EasyChair Preprint 468

Abstract

With emerging container technologies, such as Docker, microservices-based applications can be developed and deployed in cloud environment much agiler. The dependability of these microservices becomes a major concern of application providers. Anomalous behaviors which may lead to unexpected failures can be detected with anomaly detection techniques. In this paper, an anomaly detection system(ADS) is designed to detect and diagnose the anomalies in microservices by monitoring and analyzing real-time performance data of them. The proposed ADS consists of a monitoring module that collects the performance data of containers, a data processing module based on machine learning models and a fault injection module integrated for training these models. The fault injection module is also used to assess the anomaly detection and diagnosis performance of our ADS. Clearwater, an open source virtual IP Multimedia Subsystem, is used for the validation of our ADS and experimental results show that the proposed ADS works well.

Keyphrases: Microservices, anomaly detection, machine learning, performance monitoring

@booklet{EasyChair:468,
  author    = {Qingfeng Du and Tiandi Xie and Yu He},
  title     = {Anomaly Detection and Diagnosis for Container-based Microservices with Performance Monitoring},
  doi       = {10.29007/43km},
  howpublished = {EasyChair Preprint 468},
  year      = {EasyChair, 2018}}