%0 Conference Proceedings
%T A Scalable Inline Cluster Deduplication Framework for Big Data Protection
%+ National University of Defense Technology [China]
%+ University of Nebraska–Lincoln
%A Fu, Yinjin
%A Jiang, Hong
%A Xiao, Nong
%Z Part 5: Big-Data and Cloud Computing
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 13th International Middleware Conference (MIDDLEWARE)
%C Montreal, QC, Canada
%Y Priya Narasimhan
%Y Peter Triantafillou
%I Springer
%3 Middleware 2012
%V LNCS-7662
%P 354-373
%8 2012-12-03
%D 2012
%R 10.1007/978-3-642-35170-9_18
%K Big Data protection
%K cluster deduplication
%K data routing
%K superchunk
%K handprinting
%K similarity index
%K load balance
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Cluster deduplication has become a widely deployed technology in data protection services for Big Data to satisfy the requirements of service level agreement (SLA). However, it remains a great challenge for cluster deduplication to strike a sensible tradeoff between the conflicting goals of scalable deduplication throughput and high duplicate elimination ratio in cluster systems with low-end individual secondary storage nodes. We propose ∑-Dedupe, a scalable inline cluster deduplication framework, as a middleware deployable in cloud data centers, to meet this challenge by exploiting data similarity and locality to optimize cluster deduplication in inter-node and intra-node scenarios, respectively. Governed by a similarity-based stateful data routing scheme, ∑-Dedupe assigns similar data to the same backup server at the super-chunk granularity using a handprinting technique to maintain high cluster-deduplication efficiency without cross-node deduplication, and balances the workload of servers from backup clients. Meanwhile, ∑-Dedupe builds a similarity index over the traditional locality-preserved caching design to alleviate the chunk index-lookup bottleneck in each node. Extensive evaluation of our ∑-Dedupe prototype against state-of-the-art schemes, driven by real-world datasets, demonstrates that ∑-Dedupe achieves a cluster-wide duplicate elimination ratio almost as high as the high-overhead and poorly scalable traditional stateful routing scheme but at an overhead only slightly higher than that of the scalable but low duplicate-elimination-ratio stateless routing approaches.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-01555548/document
%2 https://inria.hal.science/hal-01555548/file/978-3-642-35170-9_18_Chapter.pdf
%L hal-01555548
%U https://inria.hal.science/hal-01555548
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-MIDDLEWARE
%~ IFIP-LNCS-7662