Web software traffic characteristics and failure prediction model selection

Article type: Research Article

Authors: Wang, Y.^{; *} | Lively, W.M. | Simmons, D.B.

Affiliations: Department of Computer Science, Texas A&M University, College Station, TX 77843, USA

Correspondence: [*] Corresponding author. E-mail: yongwang123@gmail.com.

Abstract: In this paper, we analyze web traffic characters and the relationship with software failures. Results indicate hourly web access traffic is the lowest from 3:00 to 4:00 am, while the traffic load gradually reaches peak between 14:00 and 16:00, before declining. In daily base, web traffic fluctuates in the 25 observed days. The hourly access hits appear in similar patterns to the software failures. The web site reliability is 0.9878. The mean time between failures is 82.03 hits. Five popular software reliability models are calibrated with real data. The validations show that Goel-Okumoto and Gompertz models accurately describe web software failures. Further investigations indicate that both models have some deviations in prediction accuracy starting from the 20th day. Using similar approach to change-point solutions, we recalibrate the models with different parameter values after 20th day. The results appear that two sets of parameter values greatly improve model prediction accuracy.

Keywords: Web, traffic, failure prediction, model selection

DOI: 10.3233/JCM-2009-0233

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 9, no. s1, pp. S23-S33, 2009