DATE: Thursday, May 11, 2006
TIME: 12:00 noon - 1:00 pm
PLACE: Wean Hall 8220

Eno Thereska

Informed Data Distribution Selection in a Self-predicting Storage System

Systems should be self-predicting. They should continuously monitor themselves and provide quantitative answers to What-If questions about hypothetical workload or resource changes. Self-prediction would significantly simplify administrators' decision making, such as acquisition planning and performance tuning, by reducing the detailed workload and internal system knowledge required. This talk describes and evaluates support for self-prediction in a cluster-based storage system and its application to What-If questions about data distribution selection.

Eno Thereska is finishing his fourth year of graduate studies. He works with Prof. Greg Ganger of the Parallel Data Lab (PDL). He is interested in building real distributed systems that are easy to manage. An approach he is currently pursuing puts sufficient instrumentation and modeling within the system, enabling it to answer several important what-if questions without outside intervention.

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