Modeling the Performance and Scalability of a SAP ERP System using an Evolutionary Algorithm

Daniel Tertilt, André Bögelsack, Helmut Krcmar

Abstract

Simulating the performance behavior of complex software systems, like Enterprise Resource Planning (ERP) systems, is a hard task due to the high number of system components when using a white box simulation approach. This paper utilizes a black box approach for establishing a simulation model for SAP ERP systems on the basis of real world performance data, which is gathered by using a synthetic benchmark. In this paper we introduce the benchmark, called Zachmanntest, and demonstrate that by using an evolutionary algorithm basing on the results of the Zachmanntest, the exact performance behavior of the ERP system can be modeled. Our work provides insights on how the algorithm is parameterized e.g. for the mutation and crossover probability, to receive optimal results. Furthermore we show that the evolutionary algorithm models the performance and scalability of an ERP system with an error less than 3.2%. With this approach we are able to build simulation models representing the exact performance behavior of a SAP ERP system with much less effort than required when using a white box simulation approach.

References

  1. Bögelsack, A., Jehle, H., Wittges, H., Schmidl, J., Krcmar, H., 2008. An Approach to Simulate Enterprise Resource Plannung Systems. 6th International Workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems. Barcelona, Spain.
  2. Curnow, H., Wichmann, B., 1976. A synthetic benchmark. In: The Computer Journal, Vol. 19 No. 1, pp. 43.
  3. Doppelhammer, J., Höppler, T., Kemper, A., Kossmann, D., 1997. Database performance in the real world: TPC-D and SAP R/3. Proceedings of the 1997 ACM SIGMOD international conference on Management of data. Tucson, Arizona, United States: ACM.
  4. Goldberg, D. E., 1989. Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Professional, Upper Saddle River, NJ, USA.
  5. Gradl, S., Bögelsack, A., Wittges, H., Krcmar, H., 2009. Layered Queuing Networks for Simulating Enterprise Resource Planning Systems. 6th International Workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems. Milano, Italy.
  6. Herbert, E., Dowsland, K., 1996. A family of genetic algorithms for the pallet loading problem. In: Annals of Operations Research, Vol. 63 No. 3, pp. 415-436.
  7. Jain, R., 1991. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling: Techniques for Experimental Design, Measurement, Simulation and Modelling, John Wiley & Sons, Inc.
  8. Justesen, P. D., 2009. Multi-objective Optimization using Evolutionary Algorithms. Department of Computer Science, University of Aarhus.
  9. Law, A. M., 2008. How to build valid and credible simulation models. Proceedings of the 40th Conference on Winter Simulation (pp. 39-47). Miami, Florida: Winter Simulation Conference.
  10. Rolia, J., Casale, G., Krishnamurthy, D., Dawson, S., Kraft, S., 2009. Predictive modelling of SAP ERP applications: challenges and solutions. Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools (pp. 1-9). Pisa, Italy: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
  11. Rolia, J., Kalbasi, A., Krishnamurthy, D., Dawson, S., 2010. Resource demand modeling for multi-tier services. Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering (pp. 207-216). San Jose, California, USA: ACM.
  12. SAP, 2010. SAP Standard Application Benchmarks. http://www.sap.com/solutions/benchmark/index.epx, accessed at 12.3.2010.
  13. Tikir, M. M., Carrington, L., Strohmaier, E., Snavely, A., 2007. A genetic algorithms approach to modeling the performance of memory-bound computations. Proceedings of the 2007 ACM/IEEE conference on Supercomputing (pp. 1-12). Reno, Nevada: ACM.
  14. Zitzler, E., Thiele, L., 1999. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. In: IEEE Transactions on Evolutionary Computation, Vol. 3 No. 4, pp. 257 - 271.
Download


Paper Citation


in Harvard Style

Tertilt D., Bögelsack A. and Krcmar H. (2012). Modeling the Performance and Scalability of a SAP ERP System using an Evolutionary Algorithm . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 112-118. DOI: 10.5220/0003971001120118


in Bibtex Style

@conference{iceis12,
author={Daniel Tertilt and André Bögelsack and Helmut Krcmar},
title={Modeling the Performance and Scalability of a SAP ERP System using an Evolutionary Algorithm},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={112-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003971001120118},
isbn={978-989-8565-10-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Modeling the Performance and Scalability of a SAP ERP System using an Evolutionary Algorithm
SN - 978-989-8565-10-5
AU - Tertilt D.
AU - Bögelsack A.
AU - Krcmar H.
PY - 2012
SP - 112
EP - 118
DO - 10.5220/0003971001120118