Software Aging Pattern Analysis of the Video on Demand System

Video-on-demand (VOD) system is an essential and widely used multimedia application. The importance of software reliability and availability has been well recognized and demanded. The phenomenon of software aging refers to the exhaustion of operating system resource, fragmentation and accumulation of errors, which results in progressive performance degradation or transient failures or even crashes of applications. The software aging patterns of a real VOD system are investigated. Firstly, the data on several system resource usage and application server are collected. Then, the Mann-Kendall test method is adopted to detect aging trend, and Sen’s slope estimator is applied to estimate the aging degree in the data sets. Finally, radial basis function (RBF) network models are constructed to model the extracted data series of systematic parameters and to predict software aging of the VOD system. In order to reduce the complexity of RBF networks and to improve its efficiency, principal component analysis (PCA) is used to reduce the dimensionality of input variables of the networks. The experimental results show that the software aging exists in the VOD system and the software aging prediction model based on RBF network is superior to the time series models in the aspects of prediction precision. Based on the models employed here, software rejuvenation policies can be triggered by actual measurements.