Abstract
This chapter details the CloudScale method. We describe its high-level process with the most important steps. We look more closely at the CloudScale method from Sect. 2.1 and detail it with respect to the developer roles executing it. We also introduce the two major method use cases. Method use case I is about analyzing a modeled system; method use case II deals with analyzing and migrating an implemented system. All discussions in this chapter are guided by the granularity of the analysis you want to perform, hence; this chapter also introduces granularity as a key concept and discusses how to find the right one.
As granularity is important for all steps of the CloudScale method, it is introduced in Sect. 5.2. As a second basis, our graphical notation is described in Sect. 5.3. The method description starts with an introduction into the CloudScale method roles in Sect. 5.4. As a core section in this chapter, Sect. 5.5 gives a detailed overall overview on the CloudScale method. Afterward, the following sections give details on all method steps: Sect. 5.6 outlines how to identify service-level objectives (SLOs), critical use cases, and their associated key scenarios from business needs; Sect. 5.7 then describes how to transform the SLOs and critical use cases into scalability, elasticity, and cost-efficiency requirements. Afterward, the two main use cases of the CloudScale method are introduced: Sect. 5.8 outlines how to use models to analyze a system’s properties, while Sect. 5.9 sketches how to analyze implemented and executable systems. Finally, Sect. 5.10 briefly describes how to realize and operate the system.
Access provided by CONRICYT-eBooks. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Brataas, G., Stav, E., Lehrig, S., Becker, S., Kopcak, G., Huljenić, D.: CloudScale: scalability management for cloud systems. In: Proceedings of International Conference on Performance Engineering (ICPE). ACM, New York (2013)
Brataas, G., Becker, S., Lehrig, S., Huljenić, D., Kopcak, G., Stupar, I.: The CloudScale method: a white paper. http://www.cloudscale-project.eu/publications/whitepapers
Koziolek, H., Schlich, B., Bilich, C., Weiss, R., Becker, S., Krogmann, K., Trifu, M., Mirandola, R., Koziolek, A.: An industrial case study on quality impact prediction for evolving service-oriented software. In: Taylor, R.N., Gall, H., Medvidovic, N. (eds.) Proceeding of the 33rd International Conference on Software Engineering (ICSE 2011), Software Engineering in Practice Track. Acceptance Rate: 18% (18/100), Waikiki, Honolulu, HI, pp. 776–785 (2011). [Online] http://doi.acm.org/10.1145/1985793.1985902
Lilja, D.: Measuring Computer Performance. Cambridge University Press, Cambridge (2000)
NIST Cloud Computing Standards Roadmap. National Institute of Standards and Technology (NIST), Technical Report 500-291 (2013). http://www.nist.gov/itl/cloud/upload/NIST_SP-500-291_Version-2_2013_June18_FINAL.pdf [Visited on 06/18/2016]
Smith, C.U., Williams, L.G.: Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software. Addison-Wesley, Boston, MA (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Brataas, G., Becker, S. (2017). The CloudScale Method. In: Becker, S., Brataas, G., Lehrig, S. (eds) Engineering Scalable, Elastic, and Cost-Efficient Cloud Computing Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-54286-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-54286-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54285-0
Online ISBN: 978-3-319-54286-7
eBook Packages: Computer ScienceComputer Science (R0)