In this paper, the authors present the design and implementation of an application-layer data 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 developed to determine the number of parallel streams, required to achieve the best network performance. This model can predict the optima l number of parallel streams with as few as three prediction points. They implement this new service in the Stork Data Scheduler, where the prediction points can be obtained using Iperf and GridFTP samplings. Their results show that the prediction cost plus the optimized transfer time is much less than the non-optimized transfer time in most cases.