Abstract
Although Cloud techniques developed rapidly in the last decade, most of the applications running on Cloud are still web-based. It is the performance uncertainty of Cloud resources that hinders the further migration of other applications, such as quality critical applications. Hence, an accurate Cloud performance model is crucial for optimized resource allocation to satisfy the quality requirements of the quality critical applications. However, the existing efforts of Cloud performance modeling focus more on the mean and variance, which cannot be leveraged to guarantee meeting the deadline miss rate of quality critical applications. To tackle the issue, a new modeling method is proposed to build performance uncertainty model of Cloud resources based on Extreme Value Theory, which can generate a proper threshold to guarantee the application’s Quality of Service (QoS). Based on our experimental data and studies, the threshold calculated by our proposed model can make the average miss rate become lower than the required 5% deadline miss rate and reduced by 77% compared with the traditional modeling method. The number of times that the deadline miss rate cannot be satisfied is also reduced by 84%.
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
Similar content being viewed by others
References
Zhao, Z., et al.: Developing and operating time critical applications in clouds: the state of the art and the switch approach. Proc. Comput. Sci. 68(43), 17–28 (2015)
Zhou, H., et al.: Dynamic real-time infrastructure planning and deployment for disaster early warning systems. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10861, pp. 644–654. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_51
Beirlant, J., Goegebeur, Y., Teugels, J., Segers, J.: Statistics of Extremes: Theory and Applications—Regression Analysis. [Wiley Series in Probability and Statistics], pp. 209–250. Wiley, New York (2004). https://doi.org/10.1002/0470012382
Zhou, H., Hu, Y., Su, J., de Laat, C., Zhao, Z.: CloudsStorm: an application-driven framework to enhance the programmability and controllability of cloud virtual infrastructures. In: Luo, M., Zhang, L.-J. (eds.) CLOUD 2018. LNCS, vol. 10967, pp. 265–280. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94295-7_18
El Kafhali, S., Salah, K.: Modeling and analysis of performance and energy consumption in cloud data centers. Arab. J. Sci. Eng. 43(12), 7789–7802 (2018)
Hwang, K., Bai, X., Shi, Y., Li, M., Chen, W.G., Wu, Y.: Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans. Parallel Distrib. Syst. 27(1), 130–143 (2015)
Khazaei, H., Miic, J., Miic, V.B., Mohammadi, N.B.: Modeling the performance of heterogeneous IAAS cloud centers. In: 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops, pp. 232–237. IEEE (2013)
Antonelli, F., Cortellessa, V., Gribaudo, M., Pinciroli, R., Trivedi, K.S., Trubiani, C.: Analytical modeling of performance indices under epistemic uncertainty applied to cloud computing systems. FGCS 102, 746–761 (2020)
He, S., Manns, G., Saunders, J., Wang, W., Pollock, L., Soffa, M.L.: A statistics-based performance testing methodology for cloud applications. In: Proceedings of the Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 188–199 (2019)
Wang, W., et al.: Testing cloud applications under cloud-uncertainty performance effects. In: ICST, pp. 81–92. IEEE (2018)
Chhetri, M.B., Chichin, S., Vo, Q.B., Kowalczyk, R.: Smart cloudbench-automated performance benchmarking of the cloud. In: 2013 IEEE Sixth International Conference on Cloud Computing, pp. 414–421. IEEE (2013)
Zhou, H., et al.: Fast resource co-provisioning for time critical applications based on networked infrastructures. In: International Conference on Cloud Computing, pp. 802–805. IEEE (2016)
Kopytov, A.: Sysbench manual. In: MySQL AB, pp. 2–3 (2012)
Acknowledgment
The work is supported by the National Natural Science Foundation of China under grant No. 62102434 and No. 62002364, and is partially supported by the Natural Science Foundation of Hunan Province under grant No. 2020JJ3042 and No. 2022JJ30667, and is also supported by the EU Horizon 2020 research and innovation program of the ENVRI-FAIR project (824068), the BLUECLOUD project (862409), and the LifeWatch ERIC project.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, M., Su, J., Liu, H., Zhao, Z., Ouyang, X., Zhou, H. (2022). The Extreme Counts: Modeling the Performance Uncertainty of Cloud Resources with Extreme Value Theory. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., Ruiz-Cortés, A. (eds) Service-Oriented Computing. ICSOC 2022. Lecture Notes in Computer Science, vol 13740. Springer, Cham. https://doi.org/10.1007/978-3-031-20984-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-20984-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20983-3
Online ISBN: 978-3-031-20984-0
eBook Packages: Computer ScienceComputer Science (R0)