rel="nofollow" href="#ulink_588e89b9-b218-598f-a84e-16c8d59084e0">21], which was elaborated upon in detail [16] and is briefly presented in the rest of the chapter. Walker’s model recognizes the fact that a CPU bought today will be two times worse (in terms of performance) than one bought for the same amount of money in two years.
3.11. Analysis and key findings
The model of the project has three levels. The first and second levels show the primary and secondary evaluation criteria, respectively, while the last level shows the alternatives for the project. Therefore, it has been decided to quantify the model by sending it to 13 experts and getting their responses that can be helpful to choose one of the choices. The responses that came from the 13 experts showed that the Technological factor was the most important primary criteria to be considered—the mean for the Technological factor calculated by all the experts was 0.369, which can be seen in Appendix B. For the secondary selection criteria, Usefulness was the most important criteria that should be considered and the mean for this factor calculated by all the experts was 0.604. The responses from the expert panel also show that the Economical factor was the second-most important criteria that should be considered—the mean for the Economical factor was 0.268. For the secondary selection criteria the responses from the expert panel showed that Security should be considered as the second-most important criteria, where the calculated mean was 0.581. The third-most important criteria was the Innovation factor with a mean of 0.191. After the final results had been collected from the 13 experts, the final calculation results using the given criteria showed that Amazon Web Services should be chosen as the cloud computing platform to develop the application, as it received the highest mean value of 0.36 (see Appendix A). However, Amazon Web Services also has a biggest standard deviation of 0.07, as compared to other alternatives. It is noteworthy that the data obtained from our panel of experts did not show a high disagreement value, which at 0.055 gives us a good indication that the experts’ opinions about the decision were very close. The advantage suggested by this low disagreement value is that there is little value in investing any further efforts to further lower the disagreement value. From the results, the second-best option for a cloud computing platform was Microsoft Azure.
3.12.Future research and limitations
This model tries to cover the important factors an application developer should consider when choosing a cloud computing platform. There are other cloud service providers to choose from, but we shall limit this model to four alternatives only. This model does not differentiate between mobile cloud computing or the different applications used in different industries.
In future research, more primary and secondary selection criteria such as SaaS, PaaS, IaaS, Backup & Recovery, Upgrade, Training & Support, etc., should be considered. A pairwise comparison for Small Scale, Medium Scale and Large Scale businesses would be worth doing. It would be interesting to see the results from the same experts for different primary and secondary criteria selection models.
3.13.Conclusion and recommendation
In conclusion, the proposition of choosing Amazon Web Services as the best cloud computing platform to develop applications was proven correct by using pairwise comparisons and the HDM. It is interesting to note that while not all individual values rated Amazon Web Services as the highest, it was the clear front-runner once all the calculations were done. The HDM is a useful tool to help decision-making or as a classification among alternatives with many different criteria to consider. In this model, multiple criteria were used in a decision regarding the best choice for a cloud platform. Even though each of the experts already had a mode of platform in mind, their answers were different once all the comparisons were made. The HDM had taken the biases out of their decisions and left them with an alternative that fit what was felt to be the most important. The use of a HDM should be made a means of decision-making to any group of individuals with a multifaceted decision to make.
References
1.H. A. Alanazi, T. U. Daim and D. F. Kocaoglu, “Identify the best alternatives to help the diffusion of teleconsultation by using the Hierarchical Decision Model (HDM)”, in Portland International Conference on Management of Engineering and Technology (PICMET), 2015.
2.M. Adnan. “Title of dissertation”, University of Minnesota, December 2011.
3.M. M. Lingga, “Developing a Hierarchical Decision Model to evaluate nuclear power plant alternative siting technologies”, PhD dissertation, Portland State University, 2016.
4.D. I. Cleland and D. F. Kocaoglu, “Hierarchical Decision Model”, Engineering Management (New York: McGraw-Hill, 1981), pp. 449–463.
5.N. J. Sheikh, K. Kim and D. F. Kocaoglu, “Use of hierarchical decision modeling to select target markets for a new personal healthcare device”, Health Policy and Technology, 5, 2 (2016) 99–112.
6.M. Abbas, “Analysis of decision inconsistencies in judgment quantification”, in Proceedings of Technology Management in the IT-Driven Services (PICMET), 2013.
7.A. A. Huczynski and D. A. Buchanan, Organisational Behaviour, 6th edition (England: Prentice Hall, 2007).
8.Y. Shin, “Conflict resolution in virtual teams”, Organisational Dynamics, 34, 4 (2005) 331–345.
9.D. L. Duart and N. T. Snyder, Mastering Virtual Teams: Strategies, Tools and Techniques That Succeed, 2nd edition (San Francisco: Jossey-Bass, 2001).
10.A. Sheth and A. Ranabahu, “Semantic modeling for cloud computing, part I & II”, IEEE Internet Computing Magazine, 14 (2010) 81–83.
11.J. McKendrick, “Does platform as a service have interoperability issues?” Zdnet.com, 2010. http://www.zdnet.com/blog/service-oriented/doesplatform-as-a-service-have-interoperability-issues/4890.
12.The Economist, “Battle of the clouds”, The Economist, 2009. https://www.economist.com/leaders/2009/10/15/battle-of-the-clouds.
13.D. Catteddu and G. Hogben, “Cloud computing—benefits, risks and recommendations for information security”, European Network and Information Security Agency, 2009. http://www.enisa.europa.eu/act/rm/files/deliverables/cloud-computingrisk-assessment/.
14.N.
