Papers
Topics
Authors
Recent
Search
2000 character limit reached

On Software Ageing Indicators in OpenStack

Published 25 Apr 2024 in cs.DC | (2404.16446v1)

Abstract: Distributed systems in general and cloud systems in particular, are susceptible to failures that can lead to substantial economic and data losses, security breaches, and even potential threats to human safety. Software ageing is an example of one such vulnerability. It emerges due to routine re-usage of computational systems units which induce fatigue within the components, resulting in an increased failure rate and potential system breakdown. Due to its stochastic nature, ageing cannot be directly measured, instead ageing indicators as proxies are used. While there are dozens of studies on different ageing indicators, their comprehensive comparison in different settings remains underexplored. In this paper, we compare two ageing indicators in OpenStack as a use case. Specifically, our evaluation compares memory usage (including swap memory) and request response time, as readily available indicators. By executing multiple OpenStack deployments with varying configurations, we conduct a series of experiments and analyze the ageing indicators. Comparative analysis through statistical tests provides valuable insights into the strengths and weaknesses of the utilised ageing indicators. Finally, through an in-depth analysis of other OpenStack failures, we identify underlying failure patterns and their impact on the studied ageing indicators.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. A. Sunyaev, “Cloud computing,” Internet Computing: Principles of Distributed Systems and Emerging Internet-Based Technologies, pp. 195–236, 2020.
  2. W. Kim, “Cloud computing: Today and tomorrow.” J. Object Technol., vol. 8, no. 1, pp. 65–72, 2009.
  3. R. Latif, H. Abbas, S. Assar, and Q. Ali, “Cloud computing risk assessment: a systematic literature review,” Future Information Technology: FutureTech 2013, pp. 285–295, 2014.
  4. A. Avižienis, J.-C. Laprie, B. Randell, and C. Landwehr, “Basic concepts and taxonomy of dependable and secure computing,” IEEE Transactions on Dependable and Secure Computing, vol. 1, no. 1, pp. 11–33, 2004.
  5. P. Notaro, J. Cardoso, and M. Gerndt, “A survey of aiops methods for failure management,” ACM Trans. Intell. Syst. Technol., vol. 12, 2021.
  6. M. Grottke, R. Matias, and K. S. Trivedi, “The fundamentals of software aging,” in 2008 IEEE International conference on software reliability engineering workshops (ISSRE Wksp).   Ieee, 2008, pp. 1–6.
  7. Y. Huang, C. Kintala, N. Kolettis, and N. D. Fulton, “Software rejuvenation: Analysis, module and applications,” in Twenty-fifth international symposium on fault-tolerant computing. Digest of papers.   IEEE, 1995, pp. 381–390.
  8. R. Pietrantuono and S. Russo, “A survey on software aging and rejuvenation in the cloud,” Software Quality Journal, vol. 28, no. 1, pp. 7–38, 2020.
  9. D. Cotroneo, R. Natella, R. Pietrantuono, and S. Russo, “Software aging and rejuvenation: Where we are and where we are going,” in 2011 IEEE Third International Workshop on Software Aging and Rejuvenation.   IEEE, 2011, pp. 1–6.
  10. J. Araujo, R. Matos, P. Maciel, R. Matias, and I. Beicker, “Experimental evaluation of software aging effects on the eucalyptus cloud computing infrastructure,” in Proceedings of the middleware 2011 industry track workshop, 2011, pp. 1–7.
  11. E. Andrade, R. Pietrantuono, F. Machida, and D. Cotroneo, “A comparative analysis of software aging in image classifiers on cloud and edge,” IEEE Transactions on Dependable and Secure Computing, 2021.
  12. M. Alhamad, T. Dillon, C. Wu, and E. Chang, “Response time for cloud computing providers,” in Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services, 2010, pp. 603–606.
  13. T. Pflanzner, R. Tornyai, B. Gibizer, A. Schmidt, and A. Kertesz, “Performance analysis of an openstack private cloud,” 2016.
  14. S. Yadav, “Comparative study on open source software for cloud computing platform: Eucalyptus, openstack and opennebula,” International Journal Of Engineering And Science, vol. 3, no. 10, pp. 51–54, 2013.
  15. T. Rosado and J. Bernardino, “An overview of openstack architecture,” in Proceedings of the 18th International Database Engineering & Applications Symposium, 2014, pp. 366–367.
  16. X. Liu, T. Embuldeniya, Z. Liu, Z. Li, M. A. Garcia, C. McAllister, and D. Schwieger, “An initial exploration of multi-node open cloud infrastructure testbed,” in Proceedings of the Conference on Information Systems Applied Research ISSN, vol. 2167, 2021, p. 1508.
  17. C. Vitucci, T. Cucinotta, R. Mancini, L. Abeni et al., “Implementation and deployment of a server at the edge using openstack components,” in Proceedings of the 19th International Conference on Networks (ICN 2020).   IARIA, 2020.
  18. R.-A. Cherrueau, D. Pertin, A. Simonet, A. Lebre, and M. Simonin, “Toward a holistic framework for conducting scientific evaluations of openstack,” in 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).   IEEE, 2017, pp. 544–548.
  19. D. L. Parnas, “Software aging,” in Proceedings of 16th International Conference on Software Engineering.   IEEE, 1994, pp. 279–287.
  20. J. Zhao, Y. Wang, G. Ning, K. S. Trivedi, R. Matias Jr, and K.-Y. Cai, “A comprehensive approach to optimal software rejuvenation,” Performance Evaluation, vol. 70, no. 11, pp. 917–933, 2013.
  21. M. Torquato and M. Vieira, “An experimental study of software aging and rejuvenation in dockerd,” in 2019 15th European Dependable Computing Conference (EDCC).   IEEE, 2019, pp. 1–6.
  22. F. Oliveira, J. Araujo, R. Matos, and P. Maciel, “Software aging in container-based virtualization: an experimental analysis on docker platform,” in 2021 16th Iberian Conference on Information Systems and Technologies (CISTI).   IEEE, 2021, pp. 1–7.
  23. F. Oliveira, J. Araujo, R. Matos, L. Lins, A. Rodrigues, and P. Maciel, “Experimental evaluation of software aging effects in a container-based virtualization platform,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).   IEEE, 2020, pp. 414–419.
  24. R. Matos, J. Araujo, V. Alves, and P. Maciel, “Characterization of software aging effects in elastic storage mechanisms for private clouds,” in 2012 IEEE 23rd International Symposium on Software Reliability Engineering Workshops.   IEEE, 2012, pp. 293–298.
  25. D. S. Kim, S. M. Lee, J.-H. Jung, T. H. Kim, S. Lee, and J. S. Park, “Reliability and availability analysis for an on board computer in a satellite system using standby redundancy and rejuvenation,” Journal of mechanical science and technology, vol. 26, pp. 2059–2063, 2012.
  26. M. D. T. de Melo, J. Araujo, I. Umesh, and P. R. M. Maciel, “Sware: an approach to support software aging and rejuvenation experiments,” Journal on Advances in Theoretical and Applied Informatics, vol. 3, no. 1, pp. 31–38, 2017.
  27. E. A. C. GUEDES, “Availability and capacity modeling for virtual network functions based on redundancy and rejuvenation supported through live migration,” 2019.
  28. H. Meng, Y. Shi, Y. Qu, J. Li, and J. Liu, “Arima-based aging prediction method for cloud server system,” in IOP Conference Series: Materials Science and Engineering, vol. 1043, no. 2.   IOP Publishing, 2021, p. 022021.
  29. J. Araujo, R. Matos, P. Maciel, and R. Matias, “Software aging issues on the eucalyptus cloud computing infrastructure,” in 2011 IEEE international conference on systems, man, and cybernetics.   IEEE, 2011, pp. 1411–1416.
  30. I. Umesh, G. Srinivasan, and M. Torquato, “Software aging forecasting using time series model,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 7, no. 3, pp. 839–845, 2017.
  31. F. Machida, A. Andrzejak, R. Matias, and E. Vicente, “On the effectiveness of mann-kendall test for detection of software aging,” in 2013 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).   IEEE, 2013, pp. 269–274.
  32. J. Alonso, R. Matias, E. Vicente, A. Maria, and K. S. Trivedi, “A comparative experimental study of software rejuvenation overhead,” Performance Evaluation, vol. 70, no. 3, pp. 231–250, 2013.
  33. C. Melo, J. Araujo, V. Alves, and P. R. M. Maciel, “Investigation of software aging effects on the openstack cloud computing platform.” J. Softw., vol. 12, no. 2, pp. 125–137, 2017.
  34. D. Cotroneo, L. De Simone, P. Liguori, R. Natella, and N. Bidokhti, “How bad can a bug get? an empirical analysis of software failures in the openstack cloud computing platform,” in Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2019, pp. 200–211.
  35. E. Gamma, R. Helm, R. Johnson, and J. Vlissides, “Design patterns: Abstraction and reuse of object-oriented design,” in ECOOP’93—Object-Oriented Programming: 7th European Conference Kaiserslautern, Germany, July 26–30, 1993 Proceedings 7.   Springer, 1993.
  36. M. Grottke, L. Li, K. Vaidyanathan, and K. S. Trivedi, “Analysis of software aging in a web server,” IEEE Transactions on reliability, vol. 55, no. 3, pp. 411–420, 2006.
  37. M. Torquato, J. Araujo, I. Umesh, and P. Maciel, “Sware: a methodology for software aging and rejuvenation experiments,” Journal of Information Systems Engineering and Management, vol. 3, no. 2, p. 15, 2018.
  38. E. Andrade, F. Machida, R. Pietrantuono, and D. Cotroneo, “Software aging in image classification systems on cloud and edge,” in 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).   IEEE, 2020, pp. 342–348.
  39. K. S. Trivedi, K. Vaidyanathan, and K. Goseva-Popstojanova, “Modeling and analysis of software aging and rejuvenation,” in Proceedings 33rd annual simulation symposium (SS 2000).   IEEE, 2000, pp. 270–279.
  40. H. B. Mann, “Nonparametric tests against trend,” Econometrica: Journal of the econometric society, pp. 245–259, 1945.
  41. P. K. Sen, “Estimates of the regression coefficient based on kendall’s tau,” Journal of the American statistical association, vol. 63, no. 324, pp. 1379–1389, 1968.
  42. R. Matias, A. Andrzejak, F. Machida, D. Elias, and K. Trivedi, “A systematic differential analysis for fast and robust detection of software aging,” in 2014 IEEE 33rd International Symposium on Reliable Distributed Systems.   IEEE, 2014, pp. 311–320.
  43. M. G. Kendall, “Rank correlation methods.” 1948.

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

Sign Up for Free