10.1184/R1/6619859.v1 Michael Abd-El-Malek Michael Abd-El-Malek William V. Courtright II William V. Courtright Chuck Cranor Chuck Cranor Gregory R. Ganger Gregory R. Ganger James Hendricks James Hendricks Andrew J. Klosterman Andrew J. Klosterman Michael Mesnier Michael Mesnier Manish Prasad Manish Prasad Brandon Salmon Brandon Salmon Raja R. Sambasivan Raja R. Sambasivan Shafeeq Sinnamohideen Shafeeq Sinnamohideen John D. Strunk John D. Strunk Eno Thereska Eno Thereska Matthew Wachs Matthew Wachs Jay J. Wylie Jay J. Wylie Ursa Minor: Versatile Cluster-based Storage (CMU-PDL-05-104) Carnegie Mellon University 2005 Data Storage 2005-12-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/Ursa_Minor_Versatile_Cluster-based_Storage_CMU-PDL-05-104_/6619859 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.