Analyzing and Forecasting the Performance of Video Service Providers

Video services are flourishing, and the same content might be hosted by different websites. A user typically does not care about which provider provides the service to him/her but rather the quality of the service. However, the network condition is not stable and it is hard to obtain the service performance in real time during the online service. Therefore, it is very important to understand the potential characteristics of video services and design the predictors to forecast the service performance. In our work, we design a measurement system deployed in 11 provinces and cities in China, monitor and analyze two popular websites, Youku and Tudou. Based on the analysis of the measured data, we see that the performance trend of two service providers has the diurnal pattern, and the worst performance is typically during the prime time, 8 - 9 pm. To understand the service structure of the website, the underlying CDN architecture is studied and the performance impact factors are analyzed. For performance forecasting, a modified time series model is proposed and evaluated, which shows that it can obtain obvious better performance than baseline models. Moreover, a new predictor by combining different information sources is designed, which can improve the forecasting precision significantly, and it may be useful in service source analyzing and recommendation systems in the future.