You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Performance Degradation Detection of Virtual Machines Via Passive Measurement and Machine Learning
Contributor:
Hayashi, Toshiaki;
Ohta, Satoru
Published:
IGI Global, 2014
Published in:
International Journal of Adaptive, Resilient and Autonomic Systems, 5 (2014) 2, Seite 40-56
Language:
Ndonga
DOI:
10.4018/ijaras.2014040103
ISSN:
1947-9239;
1947-9220
Origination:
Footnote:
Description:
<p>Virtualization is commonly used for efficient operation of servers in datacenters. The autonomic management of virtual machines enhances the advantages of virtualization. Therefore, for the development of such management, it is important to establish a method to accurately detect the performance degradation in virtual machines. This paper proposes a method that detects degradation via passive measurement of traffic exchanged by virtual machines. Using passive traffic measurement is advantageous because it is robust against heavy loads, non-intrusive to the managed machines, and independent of hardware/software platforms. From the measured traffic metrics, performance state is determined by a machine learning technique that algorithmically determines the complex relationships between traffic metrics and performance degradation from training data. The feasibility and effectiveness of the proposed method are confirmed experimentally.</p>