Extreme Binning: Scalable, Parallel Deduplication for Chunk-based File Backup
Bhagwat, Deepavali; Eshghi, Kave; Long, Darrell D.E.; Lillibridge, Mark
Keyword(s): backup, deduplication, storage
Abstract: Data deduplication is an essential and critical component of backup systems. Essential, because it reduces storage space requirements, and critical, because the performance of the entire backup operation depends on its throughput. Traditional backup workloads consist of large data streams with high locality, which existing deduplication techniques require to provide reasonable throughput. We present Extreme Binning, a scalable deduplication technique for non-traditional backup workloads that are made up of individual files with no locality among consecutive files in a given window of time. Due to lack of locality, existing techniques perform poorly on these workloads. Extreme Binning exploits file similarity instead of locality, and makes only one disk access for chunk lookup per file, which gives reasonable throughput. Multi-node backup systems built with Extreme Binning scale gracefully with the amount of input data; more backup nodes can be added very easily to boost throughput. Every file is allocated using a stateless routing algorithm to only one node, allowing for maximum parallelization, and each backup node is autonomous with no dependency across nodes, making data management tasks robust with low overhead.
Additional Publication Information: To be published in IEEE MASCOTS, 2009, London, UK, September, 21st, 2009
External Posting Date: July 6, 2009 [Fulltext]. Approved for External Publication
Internal Posting Date: July 6, 2009 [Fulltext]