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Software Aging Forecast Using Recurrent SOM with Local Model

Software Aging Forecast Using Recurrent SOM with Local Model
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Author(s): Yongquan Yan (School of Statistics, Shanxi University of Finance and Economics,, Taiyuan, China)
Copyright: 2020
Volume: 13
Issue: 1
Pages: 14
Source title: Journal of Information Technology Research (JITR)
Editor(s)-in-Chief: Francisco José García-Peñalvo (University of Salamanca, Spain)
DOI: 10.4018/JITR.2020010103

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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.

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