Application-Level Optimization of Big Data Transfers through Pipelining, Parallelism and Concurrency

@article{Yildirim2016ApplicationLevelOO,
  title={Application-Level Optimization of Big Data Transfers through Pipelining, Parallelism and Concurrency},
  author={Esma Yildirim and Engin Arslan and JangYoung Kim and Tevfik Kosar},
  journal={IEEE Transactions on Cloud Computing},
  year={2016},
  volume={4},
  pages={63-75}
}
In end-to-end data transfers, there are several factors affecting the data transfer throughput, such as the network characteristics (e.g., network bandwidth, round-trip-time, background traffic); end-system characteristics (e.g., NIC capacity, number of CPU cores and their clock rate, number of disk drives and their I/O rate); and the dataset characteristics (e.g., average file size, dataset size, file size distribution). Optimization of big data transfers over inter-cloud and intra-cloud… CONTINUE READING
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