Abstract
Safety-critical systems are designed to operate millions of hours without losing or harming life. Millions of hours enable events with small occurrence probability to materialise. Owing to this, rare events have to be factored when designing for millions of safe operating hours. In this paper, we apply a statistical paradigm named Extreme Value analysis for the modeling of the rare events and probabilistic risk assessment. Without loss of generality, our motivation is cyber-physical-systems where the IT infrastructure is frequently shared between functionally independent tasks and the run-time platform, such as Industry 4.0 based on 5G and edge cloud computing.
As a practical example, we present our method on a case study on a typical micro-service-based edge computing setup by measuring and analysing the container restart times in Kubernetes. The results can be used to asses and compare resilience mechanism design alternatives.
This paper partially relies on a previous joint project with Ericsson. Additionally, the research reported in this paper and carried out at the BME has been supported by the NRDI Fund based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology and a funding from the EU ECSEL JU under the H2020 Framework Programme, JU grant nr. 826452 (Arrowhead Tools project) and from the partners’ national funding authorities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ardagna, D., Trubian, M., Zhang, L.: SLA based resource allocation policies in autonomic environments. J. Parallel Distrib. Comput. 67(3), 259–270 (2007)
Bader, B., Yan, J.: eva: Extreme Value Analysis with Goodness-of-Fit Testing (2020)
Bernat, G., Colin, A., Petters, S.M.: WCET analysis of probabilistic hard real-time systems. In: 23rd IEEE Real-Time Systems Symposium, RTSS 2002, pp. 279–288 (2002)
Bertin, E., Clusel, M.: Generalized extreme value statistics and sum of correlated variables. J. Phys. A Math. Gen. 39(24), 7607–7619 (2006)
Bozóki, S., et al.: Application of extreme value analysis for characterizing the execution time of resilience supporting mechanisms in kubernetes. In: Bernardi, S., et al. (eds.) EDCC 2020. CCIS, vol. 1279, pp. 185–199. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58462-7_15
Bozoki, S., Pataricza, A.: Extreme value analysis for capacity design. Int. J. Cloud Comput. 7(3–4), 204–225 (2018)
Caeiro, F., Gomes, M.: Threshold Selection in Extreme Value Analysis: Methods and Applications, pp. 69–86. Taylor & Francis (2015)
Castillo, E., Hadi, A., Balakrishnan, N., Sarabia, J.: Extreme Value and Related Models with Applications in Engineering and Science. Wiley, New York, USA (2004)
Cazorla, F.J., Kosmidis, L., Mezzetti, E., Hernandez, C., Abella, J., Vardanega, T.: Probabilistic worst-case timing analysis: taxonomy and comprehensive survey. ACM Comput. Surv. 52(1), 1–35 (2019)
Cizek, P., Härdle, W.K., Weron, R.: Statistical Tools for Finance and Insurance. Springer, Berlin, Heidelberg (2011)
Cucu-Grosjean, L., et al.: Measurement-based probabilistic timing analysis for multi-path programs. In: 2012 24th Euromicro Conference on Real-Time Systems, pp. 91–101 (2012)
Cullmann, C., et al.: Predictability considerations in the design of multi-core embedded systems. In: Proceedings of Embedded Real Time Software and Systems, pp. 36–42 (2010)
Feller, W.: An introduction to probability theory and its applications. John Wiley & Sons, New York, USA (2008)
Gilleland, E., Heffernan, J.E., Stephenson, A.G.: ismev: An Introduction to Statistical Modeling of Extreme Values (2018)
Gilleland, E., Katz, R.W.: extRemes 2.0: an extreme value analysis package in R. J. Stat. Softw 72(8), 1–39 (2016)
Grinstead, C.M., Snell, J.L.: Introduction to probability. Am. Math. Soc. (2012)
McNeil, A.J., Frey, R., Embrechts, P.: Quantitative Risk Management: Concepts Techniques and Tools - Revised Edition, Revised edn. Princeton University Press, New Jersey, USA (2015)
Nadarajah, S., Zhang, Y., Pogány, T.K.: On sums of independent generalized pareto random variables with applications to insurance and cat bonds. Probab. Eng. Inf. Sci. 32(2), 296–305 (2018)
Rakoncai, P.: On Modeling and Prediction of Multivariate Extremes. Ph.D. Thesis, Mathematical Statistics Centre for Mathematical Sciences, Lund University (2009)
Scarrott, C., MacDonald, A.: A review of extreme value threshold estimation and uncertainty quantification. Revstat Stat. J. 10, 33–60 (2012)
Zhang, L., Ardagna, D.: SLA based profit optimization in autonomic computing systems. In: Proceedings of the 2nd International Conference on Service Oriented Computing, pp. 173–182 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bozóki, S., Pataricza, A. (2021). IT Design for Resiliency Using Extreme Value Analysis. In: Habli, I., Sujan, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2021. Lecture Notes in Computer Science(), vol 12852. Springer, Cham. https://doi.org/10.1007/978-3-030-83903-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-83903-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-83902-4
Online ISBN: 978-3-030-83903-1
eBook Packages: Computer ScienceComputer Science (R0)