TY - GEN
T1 - Nonparametric Distribution Models for Predicting and Managing Computational Performance Variability
AU - Lux, Thomas C.H.
AU - Watson, Layne T.
AU - Chang, Tyler H.
AU - Bernard, Jon
AU - Li, Bo
AU - Yu, Xiaodong
AU - Xu, Li
AU - Back, Godmar
AU - Butt, Ali R.
AU - Cameron, Kirk W.
AU - Hong, Yili
AU - Yao, Danfeng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056128632&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056128632&partnerID=8YFLogxK
U2 - 10.1109/SECON.2018.8478814
DO - 10.1109/SECON.2018.8478814
M3 - Conference contribution
AN - SCOPUS:85056128632
T3 - Conference Proceedings - IEEE SOUTHEASTCON
BT - Southeastcon 2018
T2 - 2018 IEEE Southeastcon, Southeastcon 2018
Y2 - 19 April 2018 through 22 April 2018
ER -