Carnegie Mellon Univsersity Parallel Data Laboratory Technical Report CMU-PDL-11-112, October, 2011.
Bin Fan, Wittawat Tantisiriroj, Lin Xiao, Garth A. Gibson
Parallel Data Laboratory
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
The first generation of Data-Intensive Scalable Computing file systems such as Google File System and Hadoop Distributed File System employed n replications for high data reliability, therefore delivering users only about 1/n of the total storage capacity of the raw disks. This paper presents DiskReduce, a framework integrating RAID into these replicated storage systems to significantly reduce the storage capacity overhead, for example, from 200% to 25% when triplicated data is dynamically replaced with RAID sets (e.g. 8 + 2 RAID 6 encoding). Based on traces collected from Yahoo!, Facebook and Opencloud cluster, we analyze (1) the capacity effectiveness of simple and not so simple strategies for grouping data blocks into RAID sets; (2) implication of reducing the number of data copies on read performance and how to overcome the degradation; and (3) different heuristics to mitigate "small write penalties." Finally, we introduce an implementation of our framework that has been built and submitted into the Apache Hadoop project.
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