Predicting network throughput for grid applications on network virtualization areas

Abstract

Grid applications are increasingly becoming dependent on network resources. Predicted network throughput is a useful parameter for network-aware scheduling for such applications. Although throughput prediction methods have been proposed, many of these methods are suffering from the fact that the probability distribution of traffic is unclear and the scale and bandwidth of networks are constantly changing. Furthermore, a virtual machine has been used as a platform for grid computing, and it can affect network measurement. A prediction method that uses pairs of differently sized connections has been proposed. This method, which we call connection pair, features a small probe transfer that predicts the throughput of a large data transfer. We propose a throughput prediction method based on the connection pair that uses ν-support vector regression (SVR) and polynomial kernel to deal with prediction models represented as a non-linear and continuous monotonic function. The prediction accuracy of our method compared to that of a previous prediction method is higher. Moreover, the drop in the accuracy is also smaller than that of the previous method under an unstable network state. We clarify the prediction accuracy with other probe sizes for the connection pair. The accuracy is decreased by a small-sized probe, and there are no changes with a large-sized probe. These results show that our method is accurate, robust, and suitable for its purpose.