Abstract
Data intensive computing applications which require access to geographically distributed data are increasingly important in the grid and cloud computing field. We designed a novel approach to the task of transferring bulk data between remote hosts, based on the fork-join queueing model, by combining, at application level, the use of classical pipes as a mean of serializing data locally with multiple parallel TCP connections which ensure high bandwidth throughput. Our new experiments in 1 Gbps and up to 9 Gbps high bandwidth networks have proven the validity of our improved multi-path transmission algorithms. Both simple file transfers and arbitrary data streaming were done effectively, efficiently, in parallel, between pairs of distributed processes connected via extended fast pipes.
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Schrager, D., Radulescu, F. (2013). An Improved Application Level Multi-path Streaming Algorithm for HBDP Networks. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_33
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DOI: https://doi.org/10.1007/978-3-642-40495-5_33
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