short-paper

ConfProf: White-Box Performance Profiling of Configuration Options

Authors:

Michael PradelAuthors Info & Claims

ICPE '21: Proceedings of the ACM/SPEC International Conference on Performance Engineering

Pages 1 - 8

https://doi.org/10.1145/3427921.3450255

Published: 09 April 2021 Publication History

Abstract

Modern software systems are highly customizable through configuration options. The sheer size of the configuration space makes it challenging to understand the performance influence of individual configuration options and their interactions under a specific usage scenario. Software with poor performance may lead to low system throughput and long response time. This paper presents ConfProf, a white-box performance profiling technique with a focus on configuration options. ConfProf helps developers understand how configuration options and their interactions influence the performance of a software system. The approach combines dynamic program analysis, machine learning, and feedback-directed configuration sampling to profile the program execution and analyze the performance influence of configuration options. Compared to existing approaches, ConfProf uses a white-box approach combined with machine learning to rank performance-influencing configuration options from execution traces. We evaluate the approach with 13 scenarios of four real-world, highly-configurable software systems. The results show that ConfProf ranks performance-influencing configuration options with high accuracy and outperform a state of the art technique.

References

[1]

Paul Anderson. The use and limitations of static-analysis tools to improve software quality. CrossTalk: The Journal of Defense Software Engineering, 21(6):18--21, 2008.

[2]

http://httpd.apache.org/docs/current/misc/perf-tuning.html.

[3]

Mona Attariyan, MIchael Chow, and Jason Flinn. X-ray: Automating root-cause diagnosis of performance anomalies in production software. In OSDI, pages 307--320, 2012.

Digital Library

[4]

Mona Attariyan and Jason Flinn. Automating configuration troubleshooting with dynamic information flow analysis. In OSDI, pages 1--11, 2010.

Digital Library

[5]

David Bailey and Allan Snavely. Performance modeling: Understanding the past and predicting the future. Euro-Par 2005 Parallel Processing, pages 620--620, 2005.

Digital Library

[6]

Jacob Burnim, Sudeep Juvekar, and Koushik Sen. Wise: Automated test generation for worst-case complexity. In ICSE, pages 463--473, 2009.

Digital Library

[7]

Tianfeng Chai and Roland R Draxler. Root mean square error (rmse) or mean absolute error (mae)?--arguments against avoiding rmse in the literature. Geoscientific model development, 7(3):1247--1250, 2014.

[8]

Monika Dhok and Murali Krishna Ramanathan. Directed test generation to detect loop inefficiencies. In FSE, 2016.

Digital Library

[9]

JianmeiGuo, K.Czarnecki, S.Apel, N.Siegmund, and A.Wasowski. Variability-aware performance prediction: A statistical learning approach. In ASE, pages 301--311, 2013.

[10]

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The WEKA data mining software: an update. SIGKDD Explorations, 11(1):10--18, 2009.

Digital Library

[11]

Xue Han, Daniel Carroll, and Tingting Yu. Reproducing performance bug reports in server applications: The researchers' experiences. Journal of Systems and Software, 156:268--282, 2019.

Digital Library

[12]

Xue Han and Tingting Yu. An empirical study on performance bugs for highly configurable software systems. In ESEM, pages 215--224, 2016.

Digital Library

[13]

Ling Huang, Jinzhu Jia, Bin Yu, Byung-Gon Chun, Petros Maniatis, and Mayur Naik. Predicting execution time of computer programs using sparse polynomial regression. In Advances in Neural Information Processing Systems, pages 883--891, 2010.

[14]

Guoliang Jin, Linhai Song, Xiaoming Shi, Joel Scherpelz, and Shan Lu. Understanding and detecting real-world performance bugs. In PLDI, pages 77--88, 2012.

Digital Library

[15]

https://redmine.lighttpd.net/projects/1/wiki/docs-performance.

[16]

Max Lillack, Christian Kästner, and Eric Bodden. Tracking load-time configuration options. IEEE Transactions on Software Engineering, 44(12):1269--1291, 2018.

Digital Library

[17]

https://machinelearningmastery.com/much-training-data-required-machine-learning/.

[18]

Adrian Nistor, Tian Jiang, and Lin Tan. Discovering, reporting, and fixing performance bugs. In MSR, pages 237--246, 2013.

[19]

Adrian Nistor, Linhai Song, Darko Marinov, and Shan Lu. Toddler: Detecting performance problems via similar memory-access patterns. In ICSE, pages 562--571, 2013.

Digital Library

[20]

http://compression.ca/pbzip2/.

[21]

https://wiki.postgresql.org/wiki/Tuning-Your-PostgreSQL-Server.

[22]

http://www.revsys.com/writings/postgresql-performance.html.

[23]

John C Platt. 12 fast training of support vector machines using sequential minimal optimization. Advances in kernel methods, pages 185--208, 1999.

[24]

Michael Pradel, Parker Schuh, George Necula, and Koushik Sen. EventBreak: Analyzing the responsiveness of user interfaces through performance-guided test generation. In OOPSLA, 2014.

[25]

Ariel Rabkin and Randy Katz. Static extraction of program configuration options. In ICSE, pages 131--140, 2011.

Digital Library

[26]

Hinrich Schütze, Christopher D Manning, and Prabhakar Raghavan.Introduction to information retrieval, volume 39. Cambridge University Press, 2008.

[27]

Marija Selakovic, Thomas Glaser, and Michael Pradel. An actionable performance profiler for optimizing the order of evaluations. In ISSTA, pages 170--180, 2017.

Digital Library

[28]

Marija Selakovic and Michael Pradel. Performance issues and optimizations in JavaScript: An empirical study. In ICSE, pages 61--72, 2016.

Digital Library

[29]

Norbert Siegmund, Alexander Grebhahn, Sven Apel, and Christian Kästner. Performance-influence models for highly configurable systems. In ESEC, pages 284--294, 2015.

Digital Library

[30]

Norbert Siegmund, Sergiy S Kolesnikov,Christian Kästner,Sven Apel, Don Batory, Marko Rosenmüller, and Gunter Saake. Predicting performance via automated feature-interaction detection. In ICSE, pages 167--177, 2012.

Digital Library

[31]

Linhai Song and Shan Lu. Statistical debugging for real-world performance problems. In OOPSLA, pages 561--578, 2014.

Digital Library

[32]

Nathan R. Tallent, John M. Mellor-Crummey, and Allan Porterfield. Analyzing lock contention in multithreaded applications. In PPoPP, pages 269--280, 2010.

Digital Library

[33]

Chong Tang. System performance optimization via design and configuration space exploration. In FSE, pages 1046--1049, 2017.

Digital Library

[34]

Alexander Tarvo and Steven P. Reiss. Automated analysis of multithreaded programs for performance modeling. In ASE, pages 7--18, 2014.

[35]

Luca Della Toffola, Michael Pradel, and Thomas R. Gross. Synthesizing programs that expose performance bottlenecks. In CGO, 2018.

Digital Library

[36]

Alexander Wert, Jens Happe, and Lucia Happe. Supporting swift reaction: Automatically uncovering performance problems by systematic experiments. In ICSE, pages 552--561, 2013.

Digital Library

[37]

Cort J Willmott. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63(11):1309--1313, 1982.

[38]

Xusheng Xiao, Shi Han, Dongmei Zhang, and Tao Xie. Context-sensitive delta inference for identifying workload-dependent performance bottlenecks. In ISSTA, pages 90--100, 2013.

Digital Library

[39]

Tingting Yu and Michael Pradel. Syncprof: Detecting, localizing, and optimizing synchronization bottlenecks. In ISSTA, pages 389--400, 2016.

Digital Library

[40]

Sai Zhang and Michael D. Ernst. Automated diagnosis of software configuration errors. In ICSE, pages 312--321, 2013.

Digital Library

Cited By

Schmid LSağlam TSelzer MKoziolek ALiem RAfzal AKousha PZhu ZLee J(2024)Cost-Efficient Construction of Performance ModelsProceedings of the 4th Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn STrategy10.1145/3660317.3660322(1-7)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3660317.3660322
Lesoil LAcher MBlouin AJézéquel J(2023)Input sensitivity on the performance of configurable systems an empirical studyJournal of Systems and Software10.1016/j.jss.2023.111671201:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111671
Velez MJamshidi PSiegmund NApel SKästner CDwyer MDamian DZeller A(2022)On debugging the performance of configurable software systemsProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510043(1571-1583)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510043

Index Terms

ConfProf: White-Box Performance Profiling of Configuration Options
1. Software and its engineering
  1. Software notations and tools

Recommendations

Static extraction of program configuration options
ICSE '11: Proceedings of the 33rd International Conference on Software Engineering

Many programs use a key-value model for configuration options. We examined how this model is used in seven open source Java projects totaling over a million lines of code. We present a static analysis that extracts a list of configuration options for a ...
White-Box Performance-Influence Models: A Profiling and Learning Approach
ICSE '21: Proceedings of the 43rd International Conference on Software Engineering

Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box ...
ConfigCrusher: towards white-box performance analysis for configurable systems
Abstract
Stakeholders of configurable systems are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and optimization processes of these systems. Several black-box ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICPE '21: Proceedings of the ACM/SPEC International Conference on Performance Engineering

April 2021

301 pages

ISBN:9781450381949

DOI:10.1145/3427921

General Chairs:
Johann Bourcier
University of Rennes 1, France
,
Zhen Ming (Jack) Jiang
York University, Canada
,
Program Chairs:
Cor-Paul Bezemer
University of Alberta, Canada
,
Vittorio Cortellessa
University of L'Aquila, Italy

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

European Research Council

Conference

ICPE '21

Sponsor:

ICPE '21: ACM/SPEC International Conference on Performance Engineering

April 19 - 23, 2021

Virtual Event, France

Acceptance Rates

ICPE '21 Paper Acceptance Rate 16 of 61 submissions, 26%;

Overall Acceptance Rate 252 of 851 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
155
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)4

Reflects downloads up to 13 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Schmid LSağlam TSelzer MKoziolek ALiem RAfzal AKousha PZhu ZLee J(2024)Cost-Efficient Construction of Performance ModelsProceedings of the 4th Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn STrategy10.1145/3660317.3660322(1-7)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3660317.3660322
Lesoil LAcher MBlouin AJézéquel J(2023)Input sensitivity on the performance of configurable systems an empirical studyJournal of Systems and Software10.1016/j.jss.2023.111671201:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111671
Velez MJamshidi PSiegmund NApel SKästner CDwyer MDamian DZeller A(2022)On debugging the performance of configurable software systemsProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510043(1571-1583)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510043
Wang JBaker TZhou YAwad AWang BZhu Y(2022)Automatic mapping of configuration options in software using static analysisJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.10.00434:10(10044-10055)Online publication date: Nov-2022
https://doi.org/10.1016/j.jksuci.2022.10.004

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents