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Bulk data transfer is facing significant challenges in the coming era of big data. There are multiple performance bottlenecks along the end-to-end path from the source to destination storage system. The limitations of current generation data transfer tools themselves can have a significant impact on end-to-end data transfer rates. In this paper, we identify the issues that lead to underperformance of these tools, and present a new data transfer tool with an innovative I/O scheduler called MDTM.doi:10.1109/lcn.2017.64 dblp:conf/lcn/ZhangDKW17 fatcat:5fw764fqmjckjpl4su7fl43kom