The week's pick
Reseach Article
Preference Analysis for Enumeration of the Most Influential Attribute of Compute Nodes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 121 - Number 22 |
| Year of Publication: 2015 |
| Authors: R. Arokia Paul Rajan, S. Ganapathy, F. Sagayaraj Francis |
10.5120/21832-5091
|
R. Arokia Paul Rajan, S. Ganapathy, F. Sagayaraj Francis . Preference Analysis for Enumeration of the Most Influential Attribute of Compute Nodes. International Journal of Computer Applications. 121, 22 ( July 2015), 17-22. DOI=10.5120/21832-5091
Abstract
Request assignment with compute nodes in a large scale distributed computing environment is a challenging research area. To devise a fitting solution, need to identify the impacting parameters and pertinent constraints originating from such an environment. This paper introduces a novel method that helps to ascertain the level of influence of each parameter among the set of parameters of cloud configurations. This work used conjoint analysis, a mathematical statistical method for enumerating the impact level of the parameters. After identifying the most influencing parameter, This work used Z-Score statistical method to quantify the capacity of the compute node into the unit of percentage. Based on this percentage split-off, the users' requests are assigned to the compute nodes. Thus the nodes are assigned to the requests based on their capacity proportion. The focus of this paper is to present the method of conducting conjoint analysis for the virtual machines' configuration in cloud. This work is the first attempt that applies conjoint analysis for identifying the impact level of parameters in the cloud architectures.
References
- Anthony Velte, Toby Velte & Robert Elsenpeter. 2009 Cloud Computing, A Practical Approach. McGraw-Hill Education.
- Armbrust, M. , Fox Griffith, A. R. , Joseph, A. D. , Katz, R. , Konwinski, A. , Lee, G. , Patterson, D. , Rabkin, A. , Stoica, I. & Zaharia, M. 2009. Above the Clouds: A Berkeley View of Cloud Computing. Retrieved November 16, 2014, from the website of University of California, Berkeley: EECS Department: www. eecs. berkeley. edu/Pubs/TechRpts/2009/EECS-2009-28. pdf
- Panneerselvam. , R. 2014. Research Methodology (2nd ed. ). New Delhi: Prentice-Hall of India.
- Darius Singpurwalla, A Handbook of Statistics. An overview of statistical methods. Available online at: http://www. e-booksdirectory. com/details. php?ebook=9440.
- Gupta. , S. C. , Kapoor, V. K. 2000. Fundamentals of Mathematical Statistics (10th ed. ). New Delhi: Sultan Chand & Sons.
- Anandasivam, A. , Best, P. , & See, S. 2010. Customers' Preferences for Infrastructure Cloud Services, Proceedings of Twelfth Conference on Commerce and Enterprise Computing. pp. 144- 149.
- Polyviou, A. , Pouloudi, N. & Rizou, S. 2014. Which Factors Affect Software-as-a-Service Selection the Most? A Study from the Customer's and the Vendor's Perspective, Proceedings of the 47th Hawaii International Conference on System Sciences. pp. 5059-5068.
- Wickremasinghe, B. , Calheiros, R. N. , & Buyya R. 2010. CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications, Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications. pp. 446-452.
- Dhinesh Babu, L. D. , & Venkata Krishna, P. 2013. Honey bee behavior inspired load balancing of tasks in cloud computing environments, Applied Soft Computing, 13 (5), pp. 2292–2303.
- Huankai Chen, F. Wang, Helian, N. & Akanmu, G. 2013. User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing, Proceedings of the National Conference on Parallel Computing Technologies. pp. 1-8.
- Fuhong Lin, Xianwei Zhou, Daochao Huang, Wei Song & Dongsheng Han. 2014. Service Scheduling in Cloud Computing based on Queuing Game Model, KSII Transactions on Internet and Information Systems, Vol. 8 (5), pp. 1554-1566.
- Bo, Z. , Ji, G. , & Jieqing, A. 2010. Cloud loading balance algorithm, Proceedings of the IEEE 2nd International Conference on Information Science and Engineering. pp. 5001–5004.
- Nishant, K. , Sharma, P. , Krishna, V. , Gupta, C. , Singh, K. P. , Nitin, N. , & Rastogi R. 2012. Load balancing of nodes in cloud using ant colony optimization, Proceedings of the 14th International Conference on in Computer Modelling and Simulation. pp. 3–8
- Ching-Chi Lin, Pangfeng Liu &Jan-Jan Wu. 2011. Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing, Proceedings of the IEEE International Conference on Cloud Computing. pp. 736-737.
- Kashyap, S. R. 2007. Algorithms for Data Placement, Reconfiguration and Monitoring in Storage Networks, Ph. D. dissertation report, University of Maryland.
- Ito, R. 2009. Job Scheduler Parameter Analysis for Evaluation of Effectiveness. Proceedings of the 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing. pp. 62-69.
- Arokia Paul Rajan & R. , Sagayaraj Francis, F. 2014. Dynamic Scheduling of Requests Based on Impacting Parameters in Cloud Based Architectures, Proceedings of the 48th Annual Convention of Computer Society of India, Advances in Intelligent Systems and Computing, Springer International Publishing, Vol. I (248). pp. 513-521.
- Arokia Paul Rajan, R. and Sagayaraj Francis, F. 2014. Experimenting with Request Assignment Simulator (RAS), International Journal on Computer Science and Engineering, vol. 6, issue 11, 363-373.
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