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DTSTAMP:20240626T180033Z
LOCATION:3001\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240626T163000
DTEND;TZID=America/Los_Angeles:20240626T164500
UID:dac_DAC 2024_sess161_RESEARCH1009@linklings.com
SUMMARY:FinerDedup: Sifting Fingerprints for Efficient Data Deduplication 
 on Mobile Devices
DESCRIPTION:Research Manuscript\n\nXianzhang Chen, Xingjie Zhou, Wei Li, X
 i Yu, Duo Liu, Yujuan Tan, and Ao Ren (Chongqing University)\n\nData dedup
 lication is promised to extend the lifetime and capacity of storage on mob
 ile devices. However, existing data deduplication works show high memory c
 onsumption and indexing costs for\nmaintaining a fingerprint for each data
  block, especially when the duplicate ratio of data blocks on mobile syste
 ms is about 10% to 30%. In this paper, we propose a novel approach called 
 FinerDedup to\noptimize the memory costs and retrieval efficiency of data 
 deduplication. FinerDedup drastically reduces the number of fingerprints b
 y screening out the duplicate data blocks via random forest and Bloom filt
 er. We implement FinerDedup on real mobile devices with Android 10 and eva
 luate it with real workloads. Extensive experimental results show that Fin
 erDedup can reduce 85% of fingerprints and 20% of I/O latency over the wid
 ely-used DmDedup.\n\nTopic: Embedded Systems\n\nKeyword: Embedded Software
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