Paper Info
Title
TDDFS: A Tier-Aware Data Deduplication-Based File System
Abstract
With the rapid increase in the amount of data produced and the development of new types of storage devices, storage tiering continues to be a popular way to achieve a good tradeoff between performance and cost-effectiveness. In a basic two-tier storage system, a storage tier with higher performance and typically higher cost (the fast tier) is used to store frequently-accessed (active) data while a large amount of less-active data are stored in the lower-performance and low-cost tier (the slow tier). Data are migrated between these two tiers according to their activity. In this article, we propose a Tier-aware Data Deduplication-based File System, called TDDFS, which can operate efficiently on top of a two-tier storage environment. Specifically, to achieve better performance, nearly all file operations are performed in the fast tier. To achieve higher cost-effectiveness, files are migrated from the fast tier to the slow tier if they are no longer active, and this migration is done with data deduplication. The distinctiveness of our design is that it maintains the non-redundant (unique) chunks produced by data deduplication in both tiers if possible. When a file is reloaded (called a reloaded file) from the slow tier to the fast tier, if some data chunks of the file already exist in the fast tier, then the data migration of these chunks from the slow tier can be avoided. Our evaluation shows that TDDFS achieves close to the best overall performance among various file-tiering designs for two-tier storage systems.
Year
DOI
Venue
2019
10.1145/3295461
ACM Transactions on Storage (TOS)
Keywords
Field
DocType
Data deduplication, data migration, file system, tiered storage
Data deduplication,File system,Computer science,Computer data storage,Parallel computing,Operating system,Data migration
Journal
Volume
Issue
ISSN
15
1
1553-3077
Citations 
PageRank 
References 
1
0.34
24
Authors
6
Name
Order
Citations
PageRank
Zhichao Cao117223.04
Hao Wen210823.70
Xiongzi Ge3648.21
Jingwei Ma47810.30
Jim Diehl5132.35
David Hung-Chang Du663474.40