research-article

Generating Workload for ERP Applications through End-User Organization Categorization using High Level Business Operation Data

Authors:

Gururaj Maddodi,

Slinger Jansen,

Rolf de JongAuthors Info & Claims

ICPE '18: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering

Pages 200 - 210

https://doi.org/10.1145/3184407.3184432

Published: 30 March 2018 Publication History

Abstract

For software companies performance testing is an essential part of new application development. In this paper we present a performance engineering method that extracts the workload of an existing legacy ERP application with more than 1 million users and generates workload for a radically new version of the application. The workload is used to classify groups of end user organizations, i.e., enterprises whose customers are end users of the application, with unsupervised machine learning techniques. The method shows that (1) workload for new application testing and architecture validation can be generated from legacy application behavior, (2) end user organizations have significantly different usage patterns, and (3) for ERP applications, high-level operations, such as a salary calculations, provide a useful method for analyzing and generating workload, as opposed to for instance low level page views. The method is evaluated within a Dutch software company, where it is found to be accurate and effective for performance engineering.

References

[1]

Sonali Aggarwal, Shashank Phadke, and Milind Bhandarkar. 2010. Characterization of hadoop jobs using unsupervised learning. In Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on. IEEE, 748--753.

Digital Library

[2]

Claudia Canali and Riccardo Lancellotti. 2017. AGATE: Adaptive Gray Area-based TEchnique to Cluster Virtual Machines with Similar Behavior. IEEE Transactions on Cloud Computing (2017).

[3]

Saurabh Chhajed. 2015. Learning ELK Stack. Packt Publishing Ltd.

[4]

Frank Elijorde and Jaewan Lee. 2015. Attaining Reliability and Energy Efficiency in Cloud Data Centers Through Workload Profiling and SLA-Aware VM Assignment. International Journal of Advances in Soft Computing & Its Applications 7, 1 (2015).

[5]

Martin Fowler. 2005. Event sourcing. Online, Dec (2005), 18.

[6]

John C Gower. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika (1966), 325--338.

[7]

Nikolas Roman Herbst, Nikolaus Huber, Samuel Kounev, and Erich Amrehn. 2014. Self-adaptive workload classification and forecasting for proactive resource provisioning. Concurrency and computation: practice and experience 26, 12 (2014), 2053--2078.

Digital Library

[8]

Zhen Jia, Jianfeng Zhan, LeiWang, Rui Han, Sally A McKee, Qiang Yang, Chunjie Luo, and Jingwei Li. 2014. Characterizing and subsetting big data workloads. In Workload Characterization (IISWC), 2014 IEEE International Symposium on. IEEE, 191--201.

[9]

Jaap Kabbedijk, Slinger Jansen, and Sjaak Brinkkemper. 2012. A case study of the variability consequences of the CQRS pattern in online business software. In Proc. of the 17th European Conference on Pattern Languages of Programs. 2:1--2:10.

Digital Library

[10]

Leonard Kaufman and Peter J Rousseeuw. 2009. Finding groups in data: an introduction to cluster analysis. Vol. 344. John Wiley & Sons.

[11]

Dao-Lei Liang and Hai-Bo Chen. 2017. An Online Mall CRM Model Based on Data Mining. In Quantitative Logic and Soft Computing 2016. Springer, 599--606.

[12]

Yanchi Liu, Zhongmou Li, Hui Xiong, Xuedong Gao, and Junjie Wu. 2010. Understanding of internal clustering validation measures. In Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, 911--916.

Digital Library

[13]

Kunal Kulkarni Xiaoyi Lu and Dhabaleswar K DK Panda. 2016. Characterizing Cloudera Impala Workloads with BigDataBench on InfiniBand Clusters. (2016).

[14]

Gururaj Maddodi, Slinger Jansen, Jan Pieter Guelen, and Rolf de Jong. 2016. The daily crash: a reflection on continuous performance testing. In 11th International Conference on Software Engineering Advances. IARIA, 171--180.

[15]

Daniel A Menascé, Virgilio AF Almeida, Rodrigo Fonseca, and Marco A Mendes. 1999. A methodology for workload characterization of e-commerce sites. In Proceedings of the 1st ACM conference on Electronic commerce. ACM, 119--128.

Digital Library

[16]

Rizwan Mian, Patrick Martin, Farhana Zulkernine, and Jose Luis Vazquez-Poletti. 2013. Towards building performance models for data-intensive workloads in public clouds. In Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering. ACM, 259--270.

Digital Library

[17]

Asit K Mishra, Joseph L Hellerstein, Walfredo Cirne, and Chita R Das. 2010. Towards characterizing cloud backend workloads: insights from Google compute clusters. ACM SIGMETRICS Performance Evaluation Review 37, 4 (2010), 34--41.

Digital Library

[18]

Ismael Solis Moreno, Peter Garraghan, Paul Townend, and Jie Xu. 2014. Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Transactions on Cloud Computing 2, 2 (2014), 208--221.

[19]

Jemishkumar Patel, Vasu Jindal, I-Ling Yen, Farokh Bastani, Jie Xu, and Peter Garraghan. 2015. Workload estimation for improving resource management decisions in the cloud. In Autonomous Decentralized Systems (ISADS), 2015 IEEE Twelfth International Symposium on. IEEE, 25--32.

Digital Library

[20]

Kai Ren, Garth Gibson, YongChul Kwon, Magdalena Balazinska, and Bill Howe. 2012. Hadoop?s Adolescence; A Comparative Workloads Analysis from Three Research Clusters. In Proceedings of 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012, (Washington, DC, USA). 1452.

Digital Library

[21]

Henk van der Schuur, Erik van de Ven, Rolf de Jong, Dennis M M Schunselaar, Hajo A Reijers, Michiel Overeem, Machiel de Graaf, Slinger Jansen, and Sjaak Brinkkemper. 2017. NEXT: Generating Tailored ERP Applications from Ontological Enterprise Models. In 10th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling (POEM).

[22]

André van Hoorn, Christian Vögele, Eike Schulz, Wilhelm Hasselbring, and Helmut Krcmar. 2014. Automatic extraction of probabilistic workload specifications for load testing session-based application systems. In Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 139--146.

Digital Library

[23]

Greg Young. {n. d.}. CQRS and Event Sourcing. Feb. 2010. URl: http://codebetter. com/gregyoung/2010/02/13/cqrs-and-event-sourcing ({n. d.}).

Cited By

Moghadam MHamidi GBorg MSaadatmand MBohlin MLisper BPotena P(2021)Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504763(2385-2394)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504763
Moghadam MSaadatmand MBorg MBohlin MLisper B(2021)An autonomous performance testing framework using self-adaptive fuzzy reinforcement learningSoftware Quality Journal10.1007/s11219-020-09532-z30:1(127-159)Online publication date: 10-Mar-2021
https://doi.org/10.1007/s11219-020-09532-z
Maddodi GJansen SOvereem MAmaral JKoziolek ATrubiani CIosup A(2020)Aggregate Architecture Simulation in Event-Sourcing Applications using Layered Queuing NetworksProceedings of the ACM/SPEC International Conference on Performance Engineering10.1145/3358960.3375797(238-245)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3358960.3375797
Show More Cited By

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Generating Workload for ERP Applications through End-User Organization Categorization using High Level Business Operation Data

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cover image ACM Conferences

ICPE '18: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering

March 2018

328 pages

ISBN:9781450350952

DOI:10.1145/3184407

General Chairs:
Katinka Wolter
Free University of Berlin, Germany
,
Will Knottenbelt
Imperial College London, UK
,
Program Chairs:
André van Hoorn
University of Stuttgart, Germany
,
Manoj Nambiar
Tata Consultancy Services, India
,
Heiko Koziolek
ABB, Germany

Copyright © 2018 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]

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Publication History

Published: 30 March 2018

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Netherlands Organisation for Scientific Research (NWO)

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ICPE '18

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ICPE '18: ACM/SPEC International Conference on Performance Engineering

April 9 - 13, 2018

Berlin, Germany

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Cited By

Moghadam MHamidi GBorg MSaadatmand MBohlin MLisper BPotena P(2021)Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504763(2385-2394)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504763
Moghadam MSaadatmand MBorg MBohlin MLisper B(2021)An autonomous performance testing framework using self-adaptive fuzzy reinforcement learningSoftware Quality Journal10.1007/s11219-020-09532-z30:1(127-159)Online publication date: 10-Mar-2021
https://doi.org/10.1007/s11219-020-09532-z
Maddodi GJansen SOvereem MAmaral JKoziolek ATrubiani CIosup A(2020)Aggregate Architecture Simulation in Event-Sourcing Applications using Layered Queuing NetworksProceedings of the ACM/SPEC International Conference on Performance Engineering10.1145/3358960.3375797(238-245)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3358960.3375797
Moghadam MSaadatmand MBorg MBohlin MLisper B(2020)Poster: Performance Testing Driven by Reinforcement Learning2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)10.1109/ICST46399.2020.00048(402-405)Online publication date: Oct-2020
https://doi.org/10.1109/ICST46399.2020.00048

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