Supporting Scalable and Adaptive Metadata Management in Ultralarge-Scale File Systems

  • Authors:

  • Yu Hua;Yifeng Zhu;Hong Jiang;Dan Feng;Lei Tian

  • Affiliations:

  • Huazhong University of Science and Technology, Wuhan;University of Maine, Orono;University of Nebraska-Lincoln, Lincoln;Huazhong University of Science and Technology, Wuhan;Huazhong University of Science and Technology, Wuhan

  • Venue:
  • IEEE Transactions on Parallel and Distributed Systems
  • Year:
  • 2011

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Abstract

This paper presents a scalable and adaptive decentralized metadata lookup scheme for ultralarge-scale file systems (more than Petabytes or even Exabytes). Our scheme logically organizes metadata servers (MDSs) into a multilayered query hierarchy and exploits grouped Bloom filters to efficiently route metadata requests to desired MDSs 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. An effective workload balance method is also developed in this paper for server reconfigurations. This scheme is evaluated through extensive trace-driven simulations and a prototype implementation in Linux. Experimental results show that this scheme can significantly improve metadata management scalability and query efficiency in ultralarge-scale storage systems.