Scalable and Adaptive Metadata Management in Ultra Large-scale File Systems, in Proceedings of the 28th International Conference on Distributed Computing Systems (ICDCS 2008), June 17-20, 2008 (acceptance rate: 16%)
Abstract
This paper presents a scalable and adaptive decentralized metadata lookup scheme for ultra large-scale file systems (larger than 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.
BibTeX Entry
	@inproceedings{zhu_icdcs08, author = {Yu Hua and Yifeng Zhu and Hong Jiang and Dan Feng and Lei Tian}, title = {Scalable and Adaptive Metadata Management in Ultra Large-Scale File Systems}, booktitle = {Proceedings of the 28th International Conference on Distributed Computing Systems ({ICDCS}'08)}, year = {2008}, pages = {401--408}, address = {Beijing, China}, }
Full Paper
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Last modified on October 16, 2007