Abstract
While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today’s 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink. Data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this paper, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulk data movement framework called LADS for terabit networks. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to use zero-copy, OS-bypass hardware when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving I/O bandwidth, and data transfer rates across the high speed networks.
Original language | English |
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Title of host publication | Proceedings of the 13th USENIX Conference on File and Storage Technologies, FAST 2015 |
Publisher | USENIX Association |
Pages | 67-80 |
Number of pages | 14 |
ISBN (Electronic) | 9781931971201 |
State | Published - 2015 |
Event | 13th USENIX Conference on File and Storage Technologies, FAST 2015 - Santa Clara, United States Duration: Feb 16 2015 → Feb 19 2015 |
Publication series
Name | Proceedings of the 13th USENIX Conference on File and Storage Technologies, FAST 2015 |
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Conference
Conference | 13th USENIX Conference on File and Storage Technologies, FAST 2015 |
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Country/Territory | United States |
City | Santa Clara |
Period | 02/16/15 → 02/19/15 |
Funding
We thank the reviewers and our shepherd, Nitin Agrawal, for their constructive comments that have significantly improved the paper. This research is sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy and used resources of the Oak Ridge Leadership Computing Facility, located in the National Center for Computational Sciences at Oak Ridge National Laboratory, which is supported by the Office of Science of the Department of Energy under Contract DE-AC05-00OR22725.