By Topic

Forecasting Software Aging of Service-Oriented Application Server Based on Wavelet Network with Adaptive Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Meng Hai Ning ; Xi'an Jiaotong Univ., Xian ; Qi Yong ; Hou Di ; Chen Ying

Web services are gaining acceptance as a standards-based approach for integrating loosely coupled services. Achieving high levels of reliability and availability of service-oriented application server in spite of service or infrastructure failures poses new challenges. According to the characteristic of performance parameters of service-oriented application sever, a new software aging forecasting model based on wavelet network is proposed. The dimensionality of input variables is reduced by principal component analysis, and the structure and parameters of wavelet network are optimized with genetic algorithm and evolutionary programming. The objective is to observe and model the existing systematic parameter data series of service-oriented application server to forecast accurately future unknown data values. By the model, we can get the aging threshold before application server fails and rejuvenate the application server in autonomic ways before observed systematic parameter value reaches the threshold. The experiments are carried out to validate the efficiency of the proposed model and show that the aging forecasting model based on wavelet network with adaptive genetic algorithm is superior to the BP neural network model and wavelet network model in the aspects of convergence rate and forecasting precision.

Published in:

Autonomic Computing, 2007. ICAC '07. Fourth International Conference on

Date of Conference:

11-15 June 2007