Paper Info
Title
RStore: A Direct-Access DRAM-based Data Store
Abstract
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.
Year
DOI
Venue
2015
10.1109/ICDCS.2015.74
International Conference on Distributed Computing Systems
Keywords
Field
DocType
Next generation networking, Data storage systems, Data processing
Dram,Distributed Computing Environment,Computer science,Computer network,Testbed,Bandwidth (signal processing),Remote direct memory access,Latency (engineering),Analytics,Data access,Operating system,Distributed computing
Conference
ISSN
Citations 
PageRank 
1063-6927
3
0.37
References 
Authors
25
6
Name
Order
Citations
PageRank
Animesh Trivedi1745.69
Patrick Stuedi226118.76
Bernard Metzler3897.72
Clemens Lutz430.37
Martin L. Schmatz515526.29
Thomas R. Gross62807404.74