Ursa minor: versatile cluster-based storage

  • Authors:
  • Michael Abd-El-Malek;William V. Courtright, II;Chuck Cranor;Gregory R. Ganger;James Hendricks;Andrew J. Klosterman;Michael Mesnier;Manish Prasad;Brandon Salmon;Raja R. Sambasivan;Shafeeq Sinnamohideen;John D. Strunk;Eno Thereska;Matthew Wachs;Jay J. Wylie

  • Affiliations:
  • Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University

  • Venue:
  • FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
  • Year:
  • 2005

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Abstract

No single encoding scheme or fault model is optimal for all data. A versatile storage system allows them to be matched to access patterns, reliability requirements, and cost goals on a per-data item basis. Ursa Minor is a cluster-based storage system that allows data-specific selection of, and on-line changes to, encoding schemes and fault models. Thus, different data types can share a scalable storage infrastructure and still enjoy specialized choices, rather than suffering from "one size fits all." Experiments with Ursa Minor show performance benefits of 2-3× when using specialized choices as opposed to a single, more general, configuration. Experiments also show that a single cluster supporting multiple workloads simultaneously is much more efficient when the choices are specialized for each distribution rather than forced to use a "one size fits all" configuration. When using the specialized distributions, aggregate cluster throughput nearly doubled.