A data throughput prediction and optimization service for widely distributed many-task computing

In this paper, we present the design and implementation of a network throughput prediction and optimization service for many-task computing in widely distributed environments. This service uses multiple parallel TCP streams to improve the end-to-end throughput of data transfers. A novel mathematical model is used to decide the number of parallel streams to achieve best performance. This model can predict the optimal number of parallel streams with as few as three prediction points. We implement this new service in the Stork data scheduler, where the prediction points can be obtained using Iperf and GridFTP samplings. Our results show that the prediction cost plus the optimized transfer time is much less than the unoptimized transfer time in most cases.