Paper Info
Title
PRO: a popularity-based multi-threaded reconstruction optimization for RAID-structured storage systems
Abstract
This paper proposes and evaluates a novel dynamic data reconstruction optimization algorithm, called popularity-based multi-threaded reconstruction optimization (PRO), which allows the reconstruction process in a RAID-structured storage system to rebuild the frequently accessed areas prior to rebuilding infrequently accessed areas to exploit access locality. This approach has the salient advantage of simultaneously decreasing reconstruction time and alleviating user and system performance degradation. It can also be easily adopted in various conventional reconstruction approaches. In particular, we optimize the disk-oriented reconstruction (DOR) approach with PRO. The PRO-powered DOR is shown to induce a much earlier onset of response-time improvement and sustain a longer time span of such improvement than the original DOR. Our benchmark studies on read-only web workloads have shown that the PRO-powered DOR algorithm consistently outperforms the original DOR algorithm in the failurerecovery process in terms of user response time, with a 3.6%-23.9% performance improvement and up to 44.7% reconstruction time improvement simultaneously.
Year
Venue
Keywords
2007
FAST
reconstruction time improvement,raid-structured storage system,reconstruction process,original dor,popularity-based multi-threaded reconstruction optimization,disk-oriented reconstruction,pro-powered dor algorithm,various conventional reconstruction approach,novel dynamic data reconstruction,pro-powered dor,multi-threaded reconstruction optimization,reconstruction time,storage system,dynamic data,system performance
Field
DocType
Citations 
Locality,Computer science,Computer data storage,Popularity,Response time,Real-time computing,Exploit,Dynamic data,RAID,Performance improvement
Conference
67
PageRank 
References 
Authors
2.22
14
8
Name
Order
Citations
PageRank
Lei Tian185339.45
Dan Feng21845188.16
Hong Jiang32137157.96
Ke Zhou445251.98
Lingfang Zeng536533.99
Jianxi Chen615310.60
Zhikun Wang723814.64
Zhenlei Song8672.22