Skip to main content
SpringerLink
Log in

A Multi-objective Performance Optimization Approach for Self-adaptive Architectures

  • Conference paper
  • First Online:
Software Architecture (ECSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12292))

Included in the following conference series:

Abstract

This paper presents an evolutionary approach for multi-objective performance optimization of Self-Adaptive Systems, represented by a specific family of Queuing Network models, namely SMAPEA QNs. The approach is based on NSGA-II genetic algorithm and it is aimed at suggesting near-optimal alternative architectures in terms of mean response times for the different available system operational modes. The evaluation is performed through a controlled experiment with respect to a realistic case study, with the aim of establishing whether meta-heuristics are worth to be investigated as a valid support to performance optimization of Self-Adaptive Systems.

Supported by the Italian Ministry of Education, University and Research – MIUR, L. 297, art. 10.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The two factors 0.812 and 1.222 have been respectively obtained by solving the equations: \(rt' + 0.1 \times rt' = rt - 0.1 \times rt\) and \(rt' - 0.1 \times rt' = rt + 0.1 \times rt\), as the adopted simulation confidence interval is \(\pm 10\%\) (0.1).

References

  1. Al-Sahaf, H., et al.: A survey on evolutionary machine learning. J. R. Soc. N. Z. 49(2), 205–228 (2019). https://doi.org/10.1080/03036758.2019.1609052

    Article  Google Scholar 

  2. Araujo, R.: Enabling configuration self-adaptation using machine learning. Ph.D. thesis, University of British Columbia (2018). https://doi.org/10.14288/1.0379346

  3. Arcelli, D.: Exploiting queuing networks to model and assess the performance of self-adaptive software systems: a survey. ANT. Procedia Comput. Sci. 170, 498–505 (2020). https://doi.org/10.1016/j.procs.2020.03.108

    Article  Google Scholar 

  4. Arcelli, D.: Towards a generalized queuing network model for self-adaptive software systems. In: MODELSWARD, pp. 457–464. SCITEPRESS (2020). https://doi.org/10.5220/0009180304570464

  5. Becker, M., Luckey, M., Becker, S.: Model-driven performance engineering of self-adaptive systems: a survey. In: QoSA, pp. 117–122. ACM (2012). https://doi.org/10.1145/2304696.2304716

  6. Bertoli, M., Casale, G., Serazzi, G.: Java modelling tools - user manual (2018). http://jmt.sourceforge.net/Papers/JMT_users_Manual.pdf

  7. Borges, R.V., d’Avila Garcez, A., Lamb, L.C., Nuseibeh, B.: Learning to adapt requirements specifications of evolving systems (Nier track). In: ICSE, ICSE 2011, pp. 856–859. ACM (2011). https://doi.org/10.1145/1985793.1985924

  8. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  9. Elkhodary, A., Esfahani, N., Malek, S.: Fusion: a framework for engineering self-tuning self-adaptive software systems. In: FSE, FSE 2010, pp. 7–16. ACM SIGSOFT (2010). https://doi.org/10.1145/1882291.1882296

  10. Faniyi, F., Lewis, P.R., Bahsoon, R., Yao, X.: Architecting self-aware software systems. In: IEEE/IFIP WICSA, WICSA 2014, pp. 91–94. IEEE Computer Society (2014). https://doi.org/10.1109/WICSA.2014.18

  11. Hellerstein, J.L., Diao, Y., Parekh, S., Tilbury, D.M.: Feedback Control of Computing Systems. Wiley, Hoboken (2004). https://doi.org/10.1002/047166880x

    Book  Google Scholar 

  12. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: With Applications in R, vol. 103. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-7138-7

    Book  MATH  Google Scholar 

  13. Jung, G., Joshi, K.R., Hiltunen, M.A., Schlichting, R.D., Pu, C.: Generating adaptation policies for multi-tier applications in consolidated server environments. In: ICAC, pp. 23–32. IEEE Computer Society (2008). https://doi.org/10.1109/ICAC.2008.21

  14. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003). https://doi.org/10.1109/MC.2003.1160055

    Article  MathSciNet  Google Scholar 

  15. Musa, J.D.: Operational profiles in software-reliability engineering. IEEE Softw. 10(2), 14–32 (1993). https://doi.org/10.1109/52.199724

    Article  Google Scholar 

  16. Shevtsov, S., Berekmeri, M., Weyns, D., Maggio, M.: Control-theoretical software adaptation: a systematic literature review. IEEE Trans. Softw. Eng. 44(8), 784–810 (2018). https://doi.org/10.1109/TSE.2017.2704579

    Article  Google Scholar 

  17. Weyns, D., Iftikhar, M.U., de la Iglesia, D.G., Ahmad, T.: A survey of formal methods in self-adaptive systems. In: C3S2E, pp. 67–79. ACM (2012). https://doi.org/10.1145/2347583.2347592

Download references

Author information

Authors and Affiliations

  1. Università degli Studi dell’Aquila, via Vetoio 1, L’Aquila, Italy

    Davide Arcelli

Authors
  1. Davide Arcelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davide Arcelli .

Editor information

Editors and Affiliations

  1. Koninklijke Philips N.V., Eindhoven, The Netherlands

    Anton Jansen

  2. VU Amsterdam, Amsterdam, The Netherlands

    Ivano Malavolta

  3. University of L’Aquila, L’Aquila, Italy

    Henry Muccini

  4. Carnegie Mellon University, Pittsburg, PA, USA

    Ipek Ozkaya

  5. University of Applied Sciences of Eastern Switzerland, Rapperswil, Switzerland

    Olaf Zimmermann

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Arcelli, D. (2020). A Multi-objective Performance Optimization Approach for Self-adaptive Architectures. In: Jansen, A., Malavolta, I., Muccini, H., Ozkaya, I., Zimmermann, O. (eds) Software Architecture. ECSA 2020. Lecture Notes in Computer Science(), vol 12292. Springer, Cham. https://doi.org/10.1007/978-3-030-58923-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58923-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58922-6

  • Online ISBN: 978-3-030-58923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation