poster

LibReDE: a library for resource demand estimation

Authors:

Simon Spinner,

Giuliano Casale,

Xiaoyun Zhu,

Samuel KounevAuthors Info & Claims

ICPE '14: Proceedings of the 5th ACM/SPEC international conference on Performance engineering

Pages 227 - 228

https://doi.org/10.1145/2568088.2576093

Published: 22 March 2014 Publication History

Get Access

Abstract

When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.

References

[1]

F. Brosig, N. Huber, and S. Kounev. Automated extraction of architecture-level performance models of distributed component-based systems. In 26th IEEE/ACM Intl. Conf. On Automated Software Engineering, Nov 2011.

Digital Library

Google Scholar

[2]

Z. Liu, L. Wynter, C. Xia, and F. Zhang. Parameter inference of queueing models for it systems using end-to-end measurements. Perf. Eval., 63(1):36--60, 2006.

Digital Library

Google Scholar

[3]

D. Menascé. Computing missing service demand parameters for performance models. In Proc. of the 2008 Computer Measurement Group (CMG) Conference, pages 241--248, 2008.

Google Scholar

[4]

J. Rolia and V. Vetland. Parameter estimation for performance models of distributed application systems. In Proc. of the 1995 conf. of the Centre for Advanced Studies on Collaborative research, page 54. IBM Press, 1995.

Digital Library

Google Scholar

[5]

W. Wang, X. Huang, X. Qin, W. Zhang, J. Wei, and H. Zhong. Application-level cpu consumption estimation: Towards performance isolation of multi-tenancy web applications. In 2012 IEEE 5th Intl. Conf. on Cloud Computing, pages 439--446, Jun 2012.

Digital Library

Google Scholar

[6]

T. Zheng, M. Woodside, and M. Litoiu. Performance model estimation and tracking using optimal filters. IEEE Trans. on Soft. Eng., 34(3):391--406, 2008.

Digital Library

Google Scholar

Cited By

View all

Avritzer ABritto RTrubiani CCamilli MJanes ARusso Bvan Hoorn AHeinrich RRapp MHenß JChalawadi R(2022)Scalability testing automation using multivariate characterization and detection of software performance antipatternsJournal of Systems and Software10.1016/j.jss.2022.111446(111446)Online publication date: Jul-2022
https://doi.org/10.1016/j.jss.2022.111446
Grohmann JEismann SBauer ASpinner SBlum JHerbst NKounev S(2021)SARDEACM Transactions on Autonomous and Adaptive Systems10.1145/346336915:2(1-31)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3463369
Mazkatli MMonschein DGrohmann JKoziolek A(2020)Incremental Calibration of Architectural Performance Models with Parametric Dependencies2020 IEEE International Conference on Software Architecture (ICSA)10.1109/ICSA47634.2020.00011(23-34)Online publication date: Mar-2020
https://doi.org/10.1109/ICSA47634.2020.00011
Show More Cited By

Index Terms

LibReDE: a library for resource demand estimation
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics

Recommendations

Modeling program resource demand using inherent program characteristics
Performance evaluation review

The workloads in modern Chip-multiprocessors (CMP) are becoming increasingly diversified, creating different resource demands on hardware substrate. It is necessary to allocate hardware resources based on the needs of the workloads in order to improve ...
Assessing Software Designs Using Capture-Recapture Methods
Special issue on software reliability

The number of faults not discovered by the design review can be estimated by using capture-recapture methods. Since these methods were developed for wildlife population estimation, the assumptions used to derive them do not match design review ...
A Resource Demand Prediction Method Based on EEMD in Cloud Computing

A large number of resources are integrated into a data center to provide various resource services in cloud computing. A major challenge is how to provide resources timely and accurately to satisfy users demands. However, users resource demands change ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICPE '14: Proceedings of the 5th ACM/SPEC international conference on Performance engineering

March 2014

310 pages

ISBN:9781450327336

DOI:10.1145/2568088

General Chairs:
Klaus-Dieter Lange
Hewlett-Packard Company, USA
,
John Murphy
University College Dublin, Ireland
,
Program Chairs:
Walter Binder
University of Lugano, Switzerland
,
José Merseguer
Universidad de Zaragoza, Spain

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2014

Check for updates

Author Tags

Qualifiers

Poster

Conference

ICPE'14

Sponsor:

ICPE'14: ACM/SPEC International Conference on Performance Engineering

March 22 - 26, 2014

Dublin, Ireland

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
215
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)2

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Avritzer ABritto RTrubiani CCamilli MJanes ARusso Bvan Hoorn AHeinrich RRapp MHenß JChalawadi R(2022)Scalability testing automation using multivariate characterization and detection of software performance antipatternsJournal of Systems and Software10.1016/j.jss.2022.111446(111446)Online publication date: Jul-2022
https://doi.org/10.1016/j.jss.2022.111446
Grohmann JEismann SBauer ASpinner SBlum JHerbst NKounev S(2021)SARDEACM Transactions on Autonomous and Adaptive Systems10.1145/346336915:2(1-31)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3463369
Mazkatli MMonschein DGrohmann JKoziolek A(2020)Incremental Calibration of Architectural Performance Models with Parametric Dependencies2020 IEEE International Conference on Software Architecture (ICSA)10.1109/ICSA47634.2020.00011(23-34)Online publication date: Mar-2020
https://doi.org/10.1109/ICSA47634.2020.00011
Bauer AHerbst NSpinner SAli-Eldin AKounev S(2019)ChameleonIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.287038930:4(800-813)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1109/TPDS.2018.2870389
Bauer ALesch VVersluis LIlyushkin AHerbst NKounev S(2019)Chamulteon: Coordinated Auto-Scaling of Micro-Services2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2019.00199(2015-2025)Online publication date: Jul-2019
https://doi.org/10.1109/ICDCS.2019.00199
Mazkatli MKoziolek AWolter KKnottenbelt Wvan Hoorn ANambiar MKoziolek H(2018)Continuous Integration of Performance ModelCompanion of the 2018 ACM/SPEC International Conference on Performance Engineering10.1145/3185768.3186285(153-158)Online publication date: 2-Apr-2018
https://dl.acm.org/doi/10.1145/3185768.3186285
von Kistowski JEismann SSchmitt NBauer AGrohmann JKounev S(2018)TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS.2018.00030(223-236)Online publication date: Sep-2018
https://doi.org/10.1109/MASCOTS.2018.00030
Bauer AGrohmann JHerbst NKounev S(2018)On the Value of Service Demand Estimation for Auto-scalingMeasurement, Modelling and Evaluation of Computing Systems10.1007/978-3-319-74947-1_10(142-156)Online publication date: 25-Jan-2018
https://doi.org/10.1007/978-3-319-74947-1_10
Kelley JStewart CMorris NTiwari DHe YElnikety S(2017)Obtaining and Managing Answer Quality for Online Data-Intensive ServicesACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/30552802:2(1-31)Online publication date: 26-Apr-2017
https://dl.acm.org/doi/10.1145/3055280
Walter JStier CKoziolek HKounev SBinder WCortellessa VKoziolek ASmirni EPoess M(2017)An Expandable Extraction Framework for Architectural Performance ModelsProceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion10.1145/3053600.3053634(165-170)Online publication date: 18-Apr-2017
https://dl.acm.org/doi/10.1145/3053600.3053634
Show More Cited By

View Options

Login options

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

Cited By

Index Terms

Recommendations

Modeling program resource demand using inherent program characteristics

Assessing Software Designs Using Capture-Recapture Methods

A Resource Demand Prediction Method Based on EEMD in Cloud Computing