publication . Preprint . 2018

An Efficient Method for Uncertainty Propagation in Robust Software Performance Estimation

Aleti, Aldeida; Trubiani, Catia; van Hoorn, André; Jamshidi, Pooyan;
Open Access English
  • Published: 14 Jan 2018
Abstract
Software engineers often have to estimate the performance of a software system before having full knowledge of the system parameters, such as workload and operational profile. These uncertain parameters inevitably affect the accuracy of quality evaluations, and the ability to judge if the system can continue to fulfil performance requirements if parameter results are different from expected. Previous work has addressed this problem by modelling the potential values of uncertain parameters as probability distribution functions, and estimating the robustness of the system using Monte Carlo-based methods. These approaches require a large number of samples, which re...
Subjects
free text keywords: Computer Science - Software Engineering
Download from
65 references, page 1 of 5

[1] A. Brunnert, A. van Hoorn, F. Willnecker, A. Danciu, W. Hasselbring, C. Heger, N. Herbst, P. Jamshidi, R. Jung, J. von Kistowski, A. Koziolek, J. Kroß, S. Spinner, C. Vo¨gele, J. Walter, A. Wert, Performance-oriented DevOps: A research agenda, Tech. Rep. SPEC-RG-2015-01, SPEC Research Group - DevOps Performance Working Group, Standard Performance Evaluation Corporation (SPEC) (2015).

[2] M. A. Marsan, G. Balbo, G. Conte, S. Donatelli, G. Franceschinis, Modelling with generalized stochastic Petri nets, John Wiley & Sons, Inc., 1994.

[3] L. Kleinrock, Queueing systems, volume i: theory.

[4] G. Franks, D. C. Petriu, C. M. Woodside, J. Xu, P. Tregunno, Layered bottlenecks and their mitigation, in: Proceedings of the International Conference on the Quantitative Evaluation of Systems, 2006, pp. 103-114.

[5] M. Woodside, G. Franks, D. C. Petriu, The future of software performance engineering, in: Proceedings of the International Workshop on Future of Software Engineering, IEEE, 2007, pp. 171-187.

[6] N. Esfahani, S. Malek, K. Razavi, Guidearch: guiding the exploration of architectural solution space under uncertainty, in: Proceedings of the International Conference on Software Engineering, 2013, pp. 43-52.

[7] P. Jamshidi, C. Pahl, N. C. Mendonc¸a, Managing uncertainty in autonomic cloud elasticity controllers, IEEE Cloud Computing 3 (3) (2016) 50-60.

[8] D. Garlan, Software engineering in an uncertain world, in: Proceedings of International Workshop on Future of Software Engineering Research, 2010, pp. 125-128.

[9] C. Trubiani, I. Meedeniya, V. Cortellessa, A. Aleti, L. Grunske, Model-based performance analysis of software architectures under uncertainty, in: Proceedings of the International Conference on Quality of Software Architectures, 2013, pp. 69-78.

[10] K. Sepahvand, S. Marburg, H.-J. Hardtke, Uncertainty quantification in stochastic systems using polynomial chaos expansion, International Journal of Applied Mechanics 2 (02) (2010) 305-353. [OpenAIRE]

[11] D. C. Petriu, H. Shen, Applying the UML performance profile: Graph grammar-based derivation of LQN models from UML specifications, in: Proceedings of the International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, Springer, 2002, pp. 159-177.

[12] I. Meedeniya, A. Aleti, L. Grunske, Architecture-driven reliability optimization with uncertain model parameters, Journal of Systems and Software 85 (10) (2012) 2340-2355.

[13] I. Meedeniya, I. Moser, A. Aleti, L. Grunske, Architecture-based reliability evaluation under uncertainty, in: Proceedings of the International Conference on Quality of Software Architectures, 2011, pp. 85-94.

[14] M. Awad, D. A. Menasc´e, On the predictive properties of performance models derived through input-output relationships, in: Proceedings of the European Workshop on Computer Performance Engineering, 2014, pp. 89-103.

[15] M. Awad, D. A. Menasc´e, Dynamic derivation of analytical performance models in autonomic computing environments, in: Proceedings of the Computer Measurement Group Conference, 2014.

65 references, page 1 of 5
Abstract
Software engineers often have to estimate the performance of a software system before having full knowledge of the system parameters, such as workload and operational profile. These uncertain parameters inevitably affect the accuracy of quality evaluations, and the ability to judge if the system can continue to fulfil performance requirements if parameter results are different from expected. Previous work has addressed this problem by modelling the potential values of uncertain parameters as probability distribution functions, and estimating the robustness of the system using Monte Carlo-based methods. These approaches require a large number of samples, which re...
Subjects
free text keywords: Computer Science - Software Engineering
Download from
65 references, page 1 of 5

[1] A. Brunnert, A. van Hoorn, F. Willnecker, A. Danciu, W. Hasselbring, C. Heger, N. Herbst, P. Jamshidi, R. Jung, J. von Kistowski, A. Koziolek, J. Kroß, S. Spinner, C. Vo¨gele, J. Walter, A. Wert, Performance-oriented DevOps: A research agenda, Tech. Rep. SPEC-RG-2015-01, SPEC Research Group - DevOps Performance Working Group, Standard Performance Evaluation Corporation (SPEC) (2015).

[2] M. A. Marsan, G. Balbo, G. Conte, S. Donatelli, G. Franceschinis, Modelling with generalized stochastic Petri nets, John Wiley & Sons, Inc., 1994.

[3] L. Kleinrock, Queueing systems, volume i: theory.

[4] G. Franks, D. C. Petriu, C. M. Woodside, J. Xu, P. Tregunno, Layered bottlenecks and their mitigation, in: Proceedings of the International Conference on the Quantitative Evaluation of Systems, 2006, pp. 103-114.

[5] M. Woodside, G. Franks, D. C. Petriu, The future of software performance engineering, in: Proceedings of the International Workshop on Future of Software Engineering, IEEE, 2007, pp. 171-187.

[6] N. Esfahani, S. Malek, K. Razavi, Guidearch: guiding the exploration of architectural solution space under uncertainty, in: Proceedings of the International Conference on Software Engineering, 2013, pp. 43-52.

[7] P. Jamshidi, C. Pahl, N. C. Mendonc¸a, Managing uncertainty in autonomic cloud elasticity controllers, IEEE Cloud Computing 3 (3) (2016) 50-60.

[8] D. Garlan, Software engineering in an uncertain world, in: Proceedings of International Workshop on Future of Software Engineering Research, 2010, pp. 125-128.

[9] C. Trubiani, I. Meedeniya, V. Cortellessa, A. Aleti, L. Grunske, Model-based performance analysis of software architectures under uncertainty, in: Proceedings of the International Conference on Quality of Software Architectures, 2013, pp. 69-78.

[10] K. Sepahvand, S. Marburg, H.-J. Hardtke, Uncertainty quantification in stochastic systems using polynomial chaos expansion, International Journal of Applied Mechanics 2 (02) (2010) 305-353. [OpenAIRE]

[11] D. C. Petriu, H. Shen, Applying the UML performance profile: Graph grammar-based derivation of LQN models from UML specifications, in: Proceedings of the International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, Springer, 2002, pp. 159-177.

[12] I. Meedeniya, A. Aleti, L. Grunske, Architecture-driven reliability optimization with uncertain model parameters, Journal of Systems and Software 85 (10) (2012) 2340-2355.

[13] I. Meedeniya, I. Moser, A. Aleti, L. Grunske, Architecture-based reliability evaluation under uncertainty, in: Proceedings of the International Conference on Quality of Software Architectures, 2011, pp. 85-94.

[14] M. Awad, D. A. Menasc´e, On the predictive properties of performance models derived through input-output relationships, in: Proceedings of the European Workshop on Computer Performance Engineering, 2014, pp. 89-103.

[15] M. Awad, D. A. Menasc´e, Dynamic derivation of analytical performance models in autonomic computing environments, in: Proceedings of the Computer Measurement Group Conference, 2014.

65 references, page 1 of 5
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue