Second Order-Based Real-Time Anomaly Detection for Application Maintenance Services

Feng Li, Qicheng Li, Lijun Mei, Shaochun Li, Liu Rong, Weiye Chen, Fenfei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Application Maintenance Services (AMS) is essential for applications executed on servers to function properly. Its objective is to reduce the application incidents happened and quickly recover services from application failures/issues. The application incidents defined as events when there are some application failures/issues happened are major concerns of AMS, therefore we propose a second order-based anomaly detection method to describe and predict application incidents based on analysis of monitored server traffic metrics. The proposed method first detects anomalies for each metric, second builds the linkage between detected anomalies for all metrics of the server and application incidents, and then predicts potential application incidents. Through the experiments, we find that the presented method provides satisfactory results for identify application incident, which gives more than 90 percentage recall rate while about 65 percentage precision rate.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on Services Science, ICSS 2015
Pages37-44
Number of pages8
ISBN (Electronic)9781479999477
DOIs
StatePublished - 5 Feb 2016
EventInternational Conference on Services Science, ICSS 2015 - Weihai, Shandong, China
Duration: 8 May 20159 May 2015

Publication series

NameProceedings of International Conference on Service Science, ICSS
Volume2016-February
ISSN (Print)2165-3836
ISSN (Electronic)2165-3828

Conference

ConferenceInternational Conference on Services Science, ICSS 2015
Country/TerritoryChina
CityWeihai, Shandong
Period8/05/159/05/15

Keywords

  • Application Maintenance Services
  • Incident Prediction
  • eal time Anomaly Detection

Fingerprint

Dive into the research topics of 'Second Order-Based Real-Time Anomaly Detection for Application Maintenance Services'. Together they form a unique fingerprint.

Cite this