TY - GEN
T1 - RStore
T2 - 35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015
AU - Trivedi, Animesh
AU - Stuedi, Patrick
AU - Metzler, Bernard
AU - Lutz, Clemens
AU - Schmatz, Martin
AU - Gross, Thomas R.
PY - 2015/7/22
Y1 - 2015/7/22
N2 - Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task. In this paper, we present RDMA Store or RStore, a DRAM-based data store that delivers high performance by extending RDMA's separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore's unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6-4.2× when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8× better than Hadoop TeraSort in a similar setting.
AB - Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task. In this paper, we present RDMA Store or RStore, a DRAM-based data store that delivers high performance by extending RDMA's separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore's unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6-4.2× when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8× better than Hadoop TeraSort in a similar setting.
KW - Data processing
KW - Data storage systems
KW - Next generation networking
UR - http://www.scopus.com/inward/record.url?scp=84944320638&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944320638&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2015.74
DO - 10.1109/ICDCS.2015.74
M3 - Conference contribution
AN - SCOPUS:84944320638
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 674
EP - 685
BT - Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 June 2015 through 2 July 2015
ER -