Netwokr Traffic Prediction Based Distributed Platform

In recent years, with the growing popularity of smart phones and the rapid development of Internet, the traditional network management system cannot adapt itself to the requirement intelligently. If we can use historic data to predict the trend of network traffic accurately, a better planning of network is more likely to be made and limited resource can be allocated and scheduled reasonably.However, massive amounts of data collected by network operators cannot be effectively processed. Therefore, in this paper, we design a network traffic prediction system based on Hadoop platform to process the real mobile network traffic data for a major network operator in China. In this paper, we analyze many network traffic prediction methods, especially the Echo State Network (ESN) method. With a new ESN structure, the system provides an effective way to process large scale network records and build two kinds of prediction models in parallel. We choose several major locations in the city, which are identified by Location Area Code, and builds prediction models to predict total request times for each location. And we select five typical mobile network services to predict traffic volume for all locations. The results of both models show that the system has achieved satisfactory effect. Consequently, the system can help network operators make a better planning of network resource and provide important references for Internet service providers.