Paper Info
Title
Scalable and Adaptive Metadata Management in Ultra Large-Scale File Systems
Abstract
This paper presents a scalable and adaptive decentralized metadata lookup scheme for ultra large-scale file systems (≥ Petabytes or even Exabytes). Our scheme logically organizes metadata servers (MDS) into a multi-layered query hierarchy and exploits grouped Bloom filters to efficiently route metadata requests to desired MDS through the hierarchy. This metadata lookup scheme can be executed at the network or memory speed, without being bounded by the performance of slow disks. Our scheme is evaluated through extensive trace-driven simulations and prototype implementation in Linux. Experimental results show that this scheme can significantly improve metadata management scalability and query efficiency in ultra large-scale storage systems.
Year
DOI
Venue
2008
10.1109/ICDCS.2008.32
ICDCS
Keywords
Field
DocType
bloom filter,organizes metadata server,multi-layered query hierarchy,metadata request,metadata management scalability,metadata lookup scheme,ultra large-scale file systems,query efficiency,ultra large-scale storage system,ultra large-scale file system,adaptive decentralized metadata lookup,adaptive metadata management,adaptive systems,file servers,linux,distributed computing,servers,broadcasting,scalability,mathematical model,storage system,meta data,bloom filters,throughput
Bloom filter,Broadcasting,Metadata,File server,Computer science,Adaptive system,Server,Metadata management,Distributed computing,Scalability
Conference
ISSN
Citations 
PageRank 
1063-6927
26
1.05
References 
Authors
30
5
Name
Order
Citations
PageRank
Yu Hua157854.94
Yifeng Zhu251335.33
Hong Jiang32137157.96
Dan Feng41845188.16
Lei Tian585339.45