Earticle

초록

영어

In order to monitor the running status of IaaS cloud computing platforms, performance metric data are collected to perform anomaly detection for IaaS cloud computing platforms and determine whether the IaaS cloud computing platforms fail to run normally. However, it is challenging to effectively detect performance anomalies from a large amount of noisy and high dimensional performance metric data. In this paper, an efficient anomaly detection scheme is proposed for IaaS cloud computing platforms. The proposed scheme first designs a global locality preserving projection algorithm to perform feature extraction on performance metric data, and then introduces a local outlier factor algorithm to detect anomalies. A series of experiments are conducted on a private cloud computing platform. Experimental results show that our proposed global locality preserving projection algorithm outperforms the principal components analysis algorithm and the locality preserving projection algorithm and our proposed anomaly detection scheme is better than the state-of-the-art schemes for IaaS cloud computing platforms.

목차

Abstract
 1. Introduction
 2. Related work
  2.1. Feature Extraction
  2.2 Anomalh Detection
 3. Proposed Anomaly Detection Scheme
  3.1. Global Locality Preserving Projection
  3.2. LOF-Based Anomaly Detection Algorithm
 4. Performance Evaluation
  4.1. Experiment Environment
  4.2. Experimental Results
 5. Conclusions
 References

키워드

저자

• Mingwei Lin [ Faculty of Software, Fujian Normal University, Fuzhou 350108, China ]
• Zhiqiang Yao [ Faculty of Software, Fujian Normal University, Fuzhou 350108, China ] Corresponding author
• Fei Gao [ Faculty of Software, Fujian Normal University, Fuzhou 350108, China ]
• Yang Li [ Faculty of Software, Fujian Normal University, Fuzhou 350108, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605