Prediction of Cloud Application’s Performance using SMTQA Tool
|International Journal of Computer Science and Engineering|
|© 2016 by SSRG - IJCSE Journal|
|Volume 3 Issue 11|
|Year of Publication : 2016|
|Authors : P.Ganesh, D EvangelinGeetha, TV Suresh Kumar|
How to Cite?
P.Ganesh, D EvangelinGeetha, TV Suresh Kumar, "Prediction of Cloud Application’s Performance using SMTQA Tool," SSRG International Journal of Computer Science and Engineering , vol. 3, no. 11, pp. 24-30, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I11P106
Performance of cloud applications is critical for its user acceptance. Resource management and scalability play important role in cloud performance. As a result, cloud environments fundamentally aim for resource consolidation and management. Also, it is challenging for the cloud service providers to allocate the cloud resources dynamically and efficiently. Through proper management of cloud resources, the scalability issue can be mitigated significantly. Given the significance of resource management in assessing the cloud application performance, we focus on evaluatingthe cloud performance considering resource utilization aspects of a cloud application. In this paper, we attempt to identifythe key actors in cloud environment with respect to resource management and design UML model for it. Also, we predict the performance of sample cloud application through SMTQA simulation.
Service Level Agreement, Service Level Objectives, Unified Modelling Language, Software Performance Engineering.
 Resource Management in Clouds: Survey and Research Challenges by Brendan Jennings and Rolf Stadler, February 2013, Springer, http://dx.doi.org/10.1007/s10922-014-9307-7
 Efficient Resource Management for Cloud Computing Environments, byAndrew J. Younge, Gregor von Laszewski, Lizhe Wang, Sonia Lopez-Alarcon, Warren Carithers
 On Resource management for cloud users: A generalised Kelly Mechanism approach, by Richard T.B. Ma, dah Ming Chiu, John C.S. Lui, Vishal Misra and Dan Rubenstein
 Cloud Computing: State-of-the-art and research challenges byQi Zhang, Lu Cheng, RaoufBoutaba, Springer Research Gate May 2010, DOI: 10.1007/s13174-010-0007-6
Resource Management and Scheduling in Cloud Environment by Vignesh V, Sendhil Kumar KS, Jaisankar N, International Journal of Scientific and Research Publications, Volume 3, Issue 6, June 2013 1 ISSN 2250-3153
 Hitoshi Matsumoto, Yutaka Ezaki,” Dynamic Resource Management in Cloud Environment”, July 2011, FUJITSU science & Tech journal, Volume 47, No: 3, page no: 270-276.
Mell P, Grance T. The NIST definition of cloud computing (draft).NIST special publication. 2011; 800(145):1–7. http://cloudpatterns.org/mechanisms/resource_ management_system
P.Ganesh, D EvangelinGeetha, T V Suresh Kumar, “Impact of resource management and scalability on performance of cloud applications – A survey”, International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.6, No.4, August 2016.
Armbrust M., Fox A., Griffith R., Joseph A.D., Katz R., Konwinski A., Lee G., Patterson D., Rabkin A., Stoica I., Zaharia M.: A view of Cloud Computing. Communications of the ACM 53(4), 50-58 (2010). DOI 10.1145/1721654.1721672
 Connie U . Smith, Performance Engineering of Software Systems, Reading, Addison-Wesley, 1990.
 D. EvangelinGeetha, T. V. Suresh Kumar, P. Mayank, K. Rajanikanth, 2010, “A tool for simulating multitier queuing applications”, Technical Report, Department of MCA, MSRIT, TRMCA 04.
 Ahn, J., Kim, C., Choi, Y.r., Huh, J.: Dynamic virtual machine scheduling in clouds for architectural shared resources. In: Proc. 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2012) (2012)
 Govindan, S., Liu, J., Kansal, A., Sivasubramaniam, A.: Cuanta: quantifying e_ects of shared on-chip resource interference for consolidated virtual machines. In: Proc. 2nd ACM Symposium on Cloud Computing (SoCC 2011), pp. 22:1-22:14. DOI 10.1145/2038916.2038938
 P.Ganesh, D EvangelinGeetha, T V Suresh Kumar, “Software Performance Engineering for Cloud Applications – A Survey”, International Journal on Recent and Innovation Trends in Computing and Communication(IJRITCC), ISSN: 2321-8169 Vol.4, No.2, February 2016.
 J. Schad, J. Dittrich, and J.A. Quiane-Ruiz, “Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance,” Proceedings of the VLDB Endowment, vol. 3, 2010, pp. 460-471.
 Jin Shao and Qianxiang Wang, “A Performance Guarantee Approach for Cloud Applications Based on Monitoring”, Proceedings of the 35th IEEE Annual Computer Software and Applications Conference Workshops, 2011.
 KhazaeiH(2012), “Performance analysis of cloud computing centers using M/G/m/m+r queuing system”, IEEE Trans Parallel Distributed Systems, 23:936-943
 Xiaodong Liu, Weiqin Tong, XiaoliZhi, Fu ZhiRen, Liao WenZhao, “Performance analysis of cloud computing services considering resources sharing among virtual machines”,Springer, Online, 20th March 2014
 Dan Marinescu, “Cloud Computing: Theory and Practice”, Elsevier Science & Technology.
 EvangelinGeetha D., Suresh Kumar T V , Rajanikanth K, Predicting the software performance during feasibility study, IET Software, April 2011, Vol.5, Issue 2, pp 201-215
 EvangelinGeetha D., Suresh Kumar T V , Rajanikanth K, Determining suitable execution environment based on dynamic workload during early stages of software development life cycle: a simulation approach, Int. Journal of Computational Science and Engg., Inderscience, Vol X., No.Y, 200X