Nonparametric Distribution Models for Predicting and Managing Computational Performance Variability

Performance variability can have a significant impact on many applications of computing. Cloud computing, high performance computing, and computer security communities each exert considerable effort managing and analyzing variability throughout the system stack. This work presents and evaluates a me...

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Bibliographic Details
Main Authors: Lux, Thomas C. H., Watson, Layne T., Chang, Tyler H., Bernard, Jon, Li, Bo, Yu, Xiaodong, Xu, Li, Back, Godmar, Butt, Ali R., Cameron, Kirk W., Hong, Yili, Yao, Danfeng
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data models
Interpolation
Mathematical model
Predictive models
Probability density function
Throughput
Online Access:Request full text
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Summary:Performance variability can have a significant impact on many applications of computing. Cloud computing, high performance computing, and computer security communities each exert considerable effort managing and analyzing variability throughout the system stack. This work presents and evaluates a methodology for predicting precise characteristics of the computational performance variability of an input/output (I/O) application over varying system configurations. Results demonstrate that the presented methodology is capable of precisely modeling performance variability, which could allow applications that tighten service level agreements, maximize computational throughput, and obfuscate system configurations against malicious users.
ISSN:1558-058X
DOI:10.1109/SECON.2018.8478814