Abstract
The paper concerns issues related to evaluation of processing in computational clouds. The effectiveness of the computational clouds depends on many factors and different metrics can be used to asses effects of processing. In this work potentiality for evaluation of processing in cloud virtual machines that takes into account also financial cost constraints is investigated. There are considered KVM and Hyper–V based cloud platform configurations. In the paper the APPI index (Application Performance and Price Index) that can be used for selection of recommended configuration considering acceptable price of processing is defined. Presented experiments show the impact of use of the proposed metric on the choice of recommended platform for processing.
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References
Cloud Computing Trends: 2017 State of the Cloud Survey. https://www.rightscale.com/blog/cloud-industry-insights/cloud-computing-trends-2017-state-cloud-survey. Accessed 21 May 2019
Kaminska, M., Smihily, M.: Cloud computing - statistics on the use by enterprises 2018. https://ec.europa.eu/eurostat/statistics-explained/index.php/. Accessed 21 May 2019
Sevcik, P.: Apdex interprets app measurements. Network World 2005. https://www.networkworld.com/article/2322637/apdex-interprets-app-measurements.html. Accessed 21 May 2019
Staś, M.: Performance evaluation of virtual machines in the computing clouds. Master’s Thesis, Wroclaw University of Science and Technology (2019)
Leitner, P., Cito, J.: Patterns in the chaos – a study of performance variation and predictability in public IaaS clouds. ACM Trans. Internet Tech. 6(3), 15 (2016)
Shankar, S., Acken, J.M., Sehgal, N.K.: Measuring performance variability in the clouds. IETE Tech. Rev. 35(6), 1–5 (2017)
Popescu, D.A., Zilberman, N., Moore, A.W.: Characterizing the impact of network latency on cloud-based applications performance. Technical report number 914, University of Cambridge - Computer Laboratory. https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-914.pdf. Accessed 21 May 2019
Dhall, C.: Scalability Patterns - Best Practices for Designing High Volume Websites. 1st edn, Apress, Berlin (2018)
Chen, T., Bahsoon, R.: Toward a smarter cloud: self-aware autoscaling of cloud configurations and resources. Computer 48(9), 93–96 (2015)
Aminm, F., Khan, M.: Web server performance evaluation in cloud computing and local environment. Master’s Thesis, School of Computing Blekinge Institute of Technology (2012)
Fraczek, J., Zajac, Ł.: Data processing performance analysis in Windows Azure cloud. Studia Informatica 34(2A), 97–112 (2013)
Habrat, K., Ładniak, M., Onderka, Z.: Efficiency analysis of web application based on cloud system. Studia Informatica 35(3), 17–28 (2014)
Everts, T.: Time Is Money - The Business Value of Web Performance, 1st edn. O’Reilly Media Inc., Sebastopol (2016)
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Fraś, M., Kwiatkowski, J., Staś, M. (2020). A Study on Effectiveness of Processing in Computational Clouds Considering Its Cost. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1050. Springer, Cham. https://doi.org/10.1007/978-3-030-30440-9_25
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DOI: https://doi.org/10.1007/978-3-030-30440-9_25
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