Earticle

초록

영어

Cloud computing platforms are complex system, which consist of a lot of software working together. Because of software defects, cloud computing platforms may has performance anomaly during runtime. In this paper, a data-driven anomaly detection method is proposed to monitor runtime performance for cloud computing platforms. The proposed method can not only detect the performance anomaly of cloud computing platforms during runtime, but also find out which performance metric results in the anomaly. A series of experiments are conducted on a real private cloud computing platform based on OpenStack and experimental results show the proposed method is better than previous anomaly detection methods for cloud computing platforms.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Anomaly Detection Techniques
  2.2. Existing Works on Anomaly Detection for Cloud Computing Platforms
 3. Data-Driven Anomaly Detection Method
  3.1. Identify Anomalies
  3.2. Pinpoint the Anomalous Performance Metrics
 4. Performance Evaluation
  4.1. Experiment Setup
  4.2. Experimental Results
 5. Conclusions
 References

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605