Nonparametric Distribution Models for Predicting and Managing Computational Performance Variability

Thomas C.H. Lux, Layne T. Watson, Tyler H. Chang, Jon Bernard, Bo Li, Xiaodong Yu, Li Xu, Godmar Back, Ali R. Butt, Kirk W. Cameron, Yili Hong, Danfeng Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationSoutheastcon 2018
ISBN (Electronic)9781538661338
DOIs
StatePublished - 1 Oct 2018
Event2018 IEEE Southeastcon, Southeastcon 2018 - St. Petersburg, United States
Duration: 19 Apr 201822 Apr 2018

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
Volume2018-April
ISSN (Print)0734-7502

Conference

Conference2018 IEEE Southeastcon, Southeastcon 2018
Country/TerritoryUnited States
CitySt. Petersburg
Period19/04/1822/04/18

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