Keywords

Data recovery (Computer science), Electronic data processing -- Distributed processing

Abstract

Recent years have witnessed an increasing demand for super data clusters. The super data clusters have reached the petabyte-scale can consist of thousands or tens of thousands storage nodes at a single site. For this architecture, reliability is becoming a great concern. In order to achieve a high reliability, data recovery and node reconstruction is a must. Although extensive research works have investigated how to sustain high performance and high reliability in case of node failures at large scale, a reverse lookup problem, namely finding the objects list for the failed node remains open. This is especially true for storage systems with high requirement of data integrity and availability, such as scientific research data clusters and etc. Existing solutions are either time consuming or expensive. Meanwhile, replication based block placement can be used to realize fast reverse lookup. However, they are designed for centralized, small-scale storage architectures. In this thesis, we propose a fast and efficient reverse lookup scheme named Group-based Shifted Declustering (G-SD) layout that is able to locate the whole content of the failed node. G-SD extends our previous shifted declustering layout and applies to large-scale file systems. Our mathematical proofs and real-life experiments show that G-SD is a scalable reverse lookup scheme that is up to one order of magnitude faster than existing schemes.

Notes

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Graduation Date

2010

Semester

Fall

Advisor

Wang, Jun

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Format

application/pdf

Identifier

CFE0003504

URL

http://purl.fcla.edu/fcla/etd/CFE0003504

Language

English

Release Date

December 2010

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

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