Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

Santiago Goméz Sáez, Vasilios Andrikopoulos, Frank Leymann


The increase of available Cloud services and providers has contributed to accelerate the development and has broaden the possibilities for building and provisioning Cloud applications in heterogeneous Cloud environments. The necessity for satisfying business and operational requirements in an agile and rapid manner has created the need for adapting traditional methods and tooling support for building and provisioning Cloud applications. Focusing on the application's performance and its evolution, we observe a lack of support for specifying, capturing, analyzing, and reasoning on the impact of using different Cloud services and configurations. This paper bridges such a gap by proposing the conceptual and tooling support to enhance Cloud application topology models to capture and analyze the evolution of the application's performance. The tooling support is built upon an existing modeling environment, which is subsequently evaluated using the MediaWiki (Wikipedia) application and its realistic workload.


  1. Andrikopoulos, V., Binz, T., Leymann, F., and Strauch, S. (2013). How to Adapt Applications for the Cloud Environment. Computing, 95(6):493-535.
  2. Andrikopoulos, V., Gómez Sáez, S., Leymann, F., and Wettinger, J. (2014a). Optimal Distribution of Applications in the Cloud. In Jarke, M., Mylopoulos, J., and Quix, C., editors, Proceedings of CAiSE'14, pages 75-90. Springer.
  3. Andrikopoulos, V., Reuter, A., Gómez Sáez, Santiago, and Leymann, F. (2014b). A GENTL Approach for Cloud Application Topologies. In Proceedings of ESOCC'14, pages 148-159. Springer.
  4. Antonescu, A.-F., Robinson, P., and Braun, T. (2012). Dynamic topology orchestration for distributed cloudbased applications. In Proceedings of NCCA'12, pages 116-123.
  5. Bahga, A. and Madisetti, V. K. (2011). Synthetic Workload Generation for Cloud Computing Applications. Journal of Software Engineering and Applications, 4:396- 410.
  6. Binz, T., Leymann, F., and Schumm, D. (2011). CMotion: A Framework for Migration of Applications into and between Clouds. In Proceedings of SOCA'11, pages 1-4. IEEE Computer Society.
  7. Brandtzaeg, E., Mohagheghi, P., and Mosser, S. (2012). Towards a domain-specific language to deploy applications in the clouds. In Proceedings of CLOUD COMPUTING'12, pages 213-218. IARIA.
  8. Brogi, A., Ibrahim, A., Soldani, J., Carrasco, J., Cubo, J., Pimentel, E., and D'Andria, F. (2014). Seaclouds: a european project on seamless management of multi-cloud applications. ACM SIGSOFT Software Engineering Notes, 39(1):1-4.
  9. di Nitto, E., Silva, M. A. A. d., Ardagna, D., Casale, G., Craciun, C. D., Ferry, N., Muntes, V., and Solberg, A. (2013). Supporting the development and operation of multi-cloud applications: The modaclouds approach. In Proceedings of SYNASC'13, pages 417-423. IEEE.
  10. Frey, S. and Hasselbring, W. (2011). The cloudmig approach: Model-based migration of software systems to cloud-optimized applications. International Journal on Advances in Software, 4(3 and 4):342-353.
  11. Gmach, D., Rolia, J., Cherkasova, L., and Kemper, A. (2007). Workload Analysis and Demand Prediction of Enterprise Data Center Applications. In Proceedings of IISWC'07, pages 171-180.
  12. Gómez Sáez, S. (2014). Design Support for Performanceaware Cloud Application (Re-)Distribution. In Proceedings of ESOCC'2014, pages 6-11. Jenaer Schriften zur Mathematik und Informatik.
  13. Gómez Sáez, S., Andrikopoulos, V., Hahn, M., Karastoyanova, D., Leymann, F., Skouradaki, M., and Vukojevic-Haupt, K. (2015). Performance and Cost Evaluation for the Migration of a Scientific Workflow Infrastructure to the Cloud. In Proceedings CLOSER'2015, pages 352-361. SciTePress.
  14. Gómez Sáez, S., Andrikopoulos, V., Leymann, F., and Strauch, S. (2014). Design Support for Performance Aware Dynamic Application (Re-)Distribution in the Cloud. IEEE Transactions on Services Computing, 8(2):225-239.
  15. Inzinger, C., Nastic, S., Sehic, S., Vögler, M., Li, F., and Dustdar, S. (2014). MADCAT A Methodology for Architecture and Deployment of Cloud Application Topologies. In Proceedings of SOSE'14. IEEE.
  16. John, L. K., Vasudevan, P., and Sabarinathan, J. (1998). Workload Characterization: Motivation, Goals and Methodology. In Proceedings of WWC'98.
  17. Kopp, O., Binz, T., Breitenbücher, U., and Leymann, F. (2013). Winery - A Modeling Tool for TOSCA-based Cloud Applications. In Proceedings of ICSOC'13, volume 8274 of LNCS, pages 700-704. Springer Berlin Heidelberg.
  18. Leymann, F., Fehling, C., Mietzner, R., Nowak, A., and Dustdar, S. (2011). Moving applications to the cloud: An approach based on application model enrichment. International Journal of Cooperative Information Systems, 20(03):307-356.
  19. Mian, R., Martin, P., and Vazquez-Poletti, J. L. (2013). Provisioning Data Analytic Workloads in a Cloud. FGCS, 29:1452-1458.
  20. Miglierina, M., Gibilisco, G., Ardagna, D., and Di Nitto, E. (2013). Model based control for multi-cloud applications. In Proceedings of MiSE'13, pages 37-43.
  21. Mirkovic, J., Faber, T., Hsieh, P., Malaiyandisamy, G., and Malaviya, R. (2010). DADL: Distributed Application Description Language. Technical Report ISI-TR-664, USC/ISI.
  22. Urdaneta, G., Pierre, G., and van Steen, M. (2009). Wikipedia workload analysis for decentralized hosting. Elsevier Computer Networks, 53(11):1830-1845.
  23. Van Hoorn, A., Rohr, M., and Hasselbring, W. (2008). Generating probabilistic and intensity-varying workload for web-based software systems. In Performance Evaluation: Metrics, Models and Benchmarks, pages 124- 143. Springer.
  24. Waizenegger, T., Wieland, M., Binz, T., Breitenbücher, U., Haupt, F., Kopp, O., Leymann, F., Mitschang, B., Nowak, A., and Wagner, S. (2013). Policy4TOSCA: A Policy-Aware Cloud Service Provisioning Approach to Enable Secure Cloud Computing. In OTM'13.
  25. Watson, B. J., Marwah, M., Gmach, D., Chen, Y., Arlitt, M., and Wang, Z. (2010). Probabilistic Performance Modeling of Virtualized Resource Allocation. In Proceedings of ICAC'10.

Paper Citation

in Harvard Style

Goméz Sáez S., Andrikopoulos V. and Leymann F. (2016). Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies . In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-182-3, pages 160-169. DOI: 10.5220/0005803501600169

in Bibtex Style

author={Santiago Goméz Sáez and Vasilios Andrikopoulos and Frank Leymann},
title={Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies
SN - 978-989-758-182-3
AU - Goméz Sáez S.
AU - Andrikopoulos V.
AU - Leymann F.
PY - 2016
SP - 160
EP - 169
DO - 10.5220/0005803501600169