Welcome to the InfoSci Platform
Reference Hub1
Software Aging Forecast Using Recurrent SOM with Local Model

Software Aging Forecast Using Recurrent SOM with Local Model

Yongquan Yan
Copyright: © 2020 |Volume: 13 |Issue: 1 |Pages: 14
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781799805458|DOI: 10.4018/JITR.2020010103
Cite Article Cite Article

MLA

Yan, Yongquan. "Software Aging Forecast Using Recurrent SOM with Local Model." JITR vol.13, no.1 2020: pp.30-43. http://doi.org/10.4018/JITR.2020010103

APA

Yan, Y. (2020). Software Aging Forecast Using Recurrent SOM with Local Model. Journal of Information Technology Research (JITR), 13(1), 30-43. http://doi.org/10.4018/JITR.2020010103

Chicago

Yan, Yongquan. "Software Aging Forecast Using Recurrent SOM with Local Model," Journal of Information Technology Research (JITR) 13, no.1: 30-43. http://doi.org/10.4018/JITR.2020010103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Studies of software aging problems are important since they are related to QoS. Previous studies have used many methods to guarantee QoS. In this article, a recurrent self-organizing map with multi-layerperceptron is proposed to forecast resource consumption in a web server which suffered from a software aging problem. First, a resource consumption series in a web server is split into p dimensional space vectors. Second, the split series is clustered into local sets by using a recurrent self-organizing map. Last, a local prediction method called multi-layerperceptron is used to predict on each local set. The results indicated that the recurrent self-organizing map with multi-layerperceptron generates a slightly better estimation than multi-layerperceptron and autoregressive integrated moving average in the resource consumption predictions of system and application level of web server.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.