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Author Ganger, Gregory R. ♦ Salmon, Brandon ♦ Mesnier, Michael ♦ Wachs, Matthew
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract Relative fitness is a new black-box approach to modeling storage devices. Whereas conventional black-box models train to predict a device's performance given "device-independent" workload characteristics, relative fitness models learn to predict the changes in performance between specific devices. There are two advantages. First, unlike conventional modeling, relative fitness does not depend entirely on workload characteristics; performance and resource utilization (e.g., cache usage) can also be used to describe a workload. This is beneficial when workload characteristics are difficult to express (e.g., temporal locality). Second, because relative fitness models are constructed for each pair of devices, changes in workload characteristics (e.g., I/O inter-arrival delay) can be modeled. Therefore, unlike a conventional model, a relative fitness model can be used by applications with a closed I/O arrival process. In this article, we present relative fitness as an evolution of the conventional model and share some early results.
Description Affiliation: Carnegie Mellon University (Mesnier, Michael; Wachs, Matthew; Salmon, Brandon; Ganger, Gregory R.)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-10
Publisher Place New York
Journal ACM SIGMETRICS Performance Evaluation Review (PERV)
Volume Number 33
Issue Number 4
Page Count 6
Starting Page 23
Ending Page 28


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Source: ACM Digital Library