Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-3337
Publication type: | Article in scientific journal |
Type of review: | Peer review (publication) |
Title: | Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code |
Authors: | Benz, Konstantin Bohnert, Thomas M. |
DOI: | 10.21256/zhaw-3337 10.1016/j.procs.2015.09.193 |
Published in: | Procedia Computer Science |
Volume(Issue): | 61 |
Page(s): | 198 |
Pages to: | 205 |
Issue Date: | 2015 |
Publisher / Ed. Institution: | Elsevier |
ISSN: | 1877-0509 |
Language: | English |
Subjects: | Cloud computing; Autoscaling; Virtualization; Elasticity; Scalability; Availability; Reliability; Performance; Western Electric rule; Statistical process control; Aspect-oriented code |
Subject (DDC): | 004: Computer science |
Abstract: | The main benefit of cloud computing lies in the elasticity of virtual resources that are provided to end users. Cloud users do not have to pay fixed hardware costs and are charged for consumption of computing resources only. While this might be an improvement for software developers who use the cloud, application users and consumers might rather be interested in paying for application performance than resource consumption. However there is little effort on providing elasticity based on performance goals instead of resource consumption. In this paper an autoscaling method is presented which aims at providing increased application performance as it is demanded by consumers. Elastic scaling is based on “statistical process monitoring and control” and “Western Electric rules”. By demonstrating the architecture of the autoscaling method and providing performance measurements gathered in an OpenStack cloud environment, we show how the injection of aspect-oriented code into cloud applications allows for improving application performance by automatically adapting the underlying virtual environment to pre-defined performance goals. The effectiveness of the autoscaling method is verified by an experiment with a test program which shows that the program executes in only half of the time which is required if no autoscaling capabilities are provided. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/16402 |
Fulltext version: | Published version |
License (according to publishing contract): | CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International |
Departement: | School of Engineering |
Organisational Unit: | Institute of Computer Science (InIT) |
Appears in collections: | Publikationen School of Engineering |
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2015_Benz_Elastic_scaling_of_cloud_application_performance.pdf | 445.48 kB | Adobe PDF | View/Open |
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Benz, K., & Bohnert, T. M. (2015). Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code. Procedia Computer Science, 61, 198–205. https://doi.org/10.21256/zhaw-3337
Benz, K. and Bohnert, T.M. (2015) ‘Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code’, Procedia Computer Science, 61, pp. 198–205. Available at: https://doi.org/10.21256/zhaw-3337.
K. Benz and T. M. Bohnert, “Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code,” Procedia Computer Science, vol. 61, pp. 198–205, 2015, doi: 10.21256/zhaw-3337.
BENZ, Konstantin und Thomas M. BOHNERT, 2015. Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code. Procedia Computer Science. 2015. Bd. 61, S. 198–205. DOI 10.21256/zhaw-3337
Benz, Konstantin, and Thomas M. Bohnert. 2015. “Elastic Scaling of Cloud Application Performance Based on Western Electric Rules by Injection of Aspect-Oriented Code.” Procedia Computer Science 61: 198–205. https://doi.org/10.21256/zhaw-3337.
Benz, Konstantin, and Thomas M. Bohnert. “Elastic Scaling of Cloud Application Performance Based on Western Electric Rules by Injection of Aspect-Oriented Code.” Procedia Computer Science, vol. 61, 2015, pp. 198–205, https://doi.org/10.21256/zhaw-3337.
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