HARP: Predictive Transfer Optimization Based on Historical Analysis and Real-Time Probing

Abstract

Increasingly data-intensive scientific and commercial applications require frequent movement of large datasets from one site to the other. Despite the growing capacity of the networking capacity, these data movements rarely achieve the promised data transfer rates of the underlying physical network due to the poorly tuned data transfer protocols. Accurately and efficiently tuning the data transfer protocol parameters in a dynamically changing network environment is a big challenge and still an open research problem. In this paper, we present predictive end-to-end data transfer optimization algorithms based on historical data analysis and real-time background traffic probing, dubbed HARP. Most of the existing work in this area is solely based on real time network probing, which either cause too much sampling overhead or fail to accurately predict the correct transfer parameters. Combining historical data analysis with real time sampling enables our algorithms to tune the application level data transfer parameters accurately and efficiently to achieve close-to-optimal end-to-end data transfer throughput with very low overhead. Our experimental analysis over a variety of network settings shows that HARP outperforms existing solutions by up to 50% in terms of the achieved throughput.