Abstract
Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing virtual machine instance with additional resources, or by adding an additional virtual machine instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” instances or less ”large” instances. The first hypothesis states that better performance is obtained by using more and smaller instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger instances results with better performance and that the user gets more performances than expected by scaling the resources.
Keywords
- Cloud Computing
- Microsoft Azure
- Performance
- SaaS
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References
Agarwal, D., Prasad, S.K.: AzureBench: Benchmarking the storage services of the Azure cloud platform. In: Proc. of the IEEE 26th Int. Parallel and Distributed Processing Symp. Workshops & PhD Forum, IPDPSW 2012, pp. 1048–1057 (2012)
Brebner, P., Liu, A.: Performance and cost assessment of cloud services. In: Maximilien, E.M., Rossi, G., Yuan, S.-T., Ludwig, H., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6568, pp. 39–50. Springer, Heidelberg (2011)
Gao, J., Pattabhiraman, P., Bai, X., Tsai, W.: SaaS performance and scalability evaluation in clouds. In: 2011 IEEE 6th International Symposium on Service Oriented System Engineering (SOSE), pp. 61–71 (2011)
Gaster, B.: PhluffyFotos on Windows Azure (October 2012), http://www.bradygaster.com/post/phluffyfotos-on-windows-azure
Gusev, M., Ristov, S.: Superlinear speedup in Windows Azure cloud. In: 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), Paris, France, pp. 173–175 (2012)
Gusev, M., Ristov, S.: A superlinear speedup region for matrix multiplication. Concurrency and Computation: Practice and Experience 26(11), 1847–1868 (2013), http://dx.doi.org/10.1002/cpe.3102
Gusev, M., Ristov, S.: Resource scaling performance for cache intensive algorithms in Windows Azure. In: Zavoral, F., Jung, J.J., Badica, C. (eds.) IDC 2013. SCI, vol. 511, pp. 77–86. Springer, Heidelberg (2013)
Gusev, M., Ristov, S., Velkoski, G., Simjanoska, M.: Optimal resource allocation to host web services in cloud. In: Proceedings of the 2013 IEEE 6th International Conference on Cloud Computing, CLOUD 2013, CA, USA, pp. 948–949 (June 2013)
Gusev, P., Ristov, S., Gusev, M.: Performance analysis of SaaS ticket management systems. In: 2014 Federated Conference on Computer Science and Information Systems (FedCSIS) (SCoDiS-LaSCoG’14 Workshop) (in press, September 2014)
Gustafson, J.L.: Reevaluating Amdahl’s law. Communication of ACM 31(5), 532–533 (1988)
Hill, Z., Li, J., Mao, M., Ruiz-Alvarez, A., Humphrey, M.: Early observations on the performance of Windows Azure. In: Proc. of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, pp. 367–376 (2010)
Kondo, D., Javadi, B., Malecot, P., Cappello, F., Anderson, D.: Cost-benefit analysis of cloud computing versus desktop grids. In: IEEE International Symposium on Parallel Distributed Processing, IPDPS 2009, pp. 1–12 (2009)
Lu, W., Jackson, J., Ekanayake, J., Barga, R.S., Araujo, N.: Performing large science experiments on Azure: Pitfalls and solutions. In: CloudCom 2010, pp. 209–217 (2010)
Mao, M., Li, J., Humphrey, M.: Cloud auto-scaling with deadline and budget constraints. In: 2010 11th IEEE/ACM International Conference on Grid Computing (GRID), pp. 41–48 (2010)
Microsoft: Picture gallery service (April 2008), http://phluffyfotos.codeplex.com/
Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: A performance analysis of EC2 cloud computing services for scientific computing. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) Cloud Computing. LNICST, vol. 34, pp. 115–131. Springer, Heidelberg (2010)
Tudoran, R., Costan, A., Antoniu, G., Bougé, L.: A performance evaluation of Azure and Nimbus clouds for scientific applications. In: Proc. of the 2nd Int. Workshop on Cloud Computing Platforms, CloudCP 2012, pp. 4:1–4:6. ACM (2012)
Zhang, L., Ma, X., Lu, J., Xie, T., Tillmann, N., de Halleux, P.: Environmental modeling for automated cloud application testing. IEEE Software 29(2), 30–35 (2012)
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Gusev, M., Ristov, S., Koteska, B., Velkoski, G. (2014). Windows Azure: Resource Organization Performance Analysis. In: Villari, M., Zimmermann, W., Lau, KK. (eds) Service-Oriented and Cloud Computing. ESOCC 2014. Lecture Notes in Computer Science, vol 8745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44879-3_2
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DOI: https://doi.org/10.1007/978-3-662-44879-3_2
Publisher Name: Springer, Berlin, Heidelberg
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