A Data Throughput Prediction and Optimization Service for Widely Distributed Many-Task Computing

作者：Yin Dengpan^*; Yildirim Esma; Kulasekaran Sivakumar; Ross Brandon; Kosar Tevfik

来源：IEEE Transactions on Parallel and Distributed Systems, 2011, 22(6): 899-909.

DOI：10.1109/TPDS.2010.187

• Many-task computing
• modeling
• scheduling
• parallel TCP streams
• optimization
• prediction
• stork

In this paper, we 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 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 nonoptimized transfer time in most cases. As a result, Stork data transfer jobs with optimization service can be completed much earlier, compared to nonoptimized data transfer jobs.

• 出版日期2011-6