An approach to prioritize quality dimensions of based on cloud computing using Multiple Criteria Decision Making method
Subject Areas : ICT
Zahra Abbasi
1
,
Somayeh Fatahi
2
,
Mohammad Javad Ershadi
3
1 - Islamic Azad University
2 - Iranian Research Institute for Information Science and Technology (IRANDOC)
3 - IranDoc
Keywords: Cloud Computing, Fuzzy Delphi Method, Fuzzy Hierarchy Analysis, Fuzzy TOPSIS, Multi-Criteria Decision Making,
Abstract :
Today, quality is one of the most important factors in attracting customer satisfaction and loyalty to service organizations. Therefore, one of the main concerns of managers is to improve the quality of services. With the development of the Internet and the world of communications, a concept called cloud computing has expanded in the world of communications, which provides a new model for the supply, consumption and delivery of computing services. The purpose of this study is to make the optimal decision in choosing the appropriate cloud service according to the conditions of users so that they achieve the highest satisfaction. Fuzzy Delphi method, fuzzy hierarchical analysis method, fuzzy TOPSIS method and finally multi-criteria decision making method are the methods used in this research. The results of the fuzzy Delphi method show that the indicators of transparency, accessibility and reliability should be eliminated. The results of fuzzy hierarchical analysis identified the cost index as the most important index and the support index during demand as the least important index. According to the results of fuzzy TOPSIS based on the weights obtained from fuzzy hierarchical analysis, SAAS, IAAS and PAAS cloud services were ranked first to third, respectively. Using the SAAS service provides numerous benefits to employees and companies, such as reducing time and money spent on time-consuming tasks such as installing, managing, and upgrading software.
• Abbas, A. M., & Kure, O. (2010). Quality of Service in mobile ad hoc networks: a survey. International journal of ad hoc and ubiquitous computing, 6(2), 75-98.
• Aznoli, F., & Navimipour, N. J. (2017). Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. Journal of Network and Computer Applications, 77, 73-86.
• Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
• Del Castillo, A. S., & Sardi, N. (2012). ISO standards and the quality concept applied to anesthesia services. Revista Colombiana de Anestesiología, 40(1), 14-16.
• Erdil, S. T., & Yıldız, O. (2011). Measuring service quality and a comparative analysis in the passenger carriage of airline industry. Procedia-Social and Behavioral Sciences, 24, 1232-1242.
• Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207-228.
• Gui, Z., Yang, C., Xia, J., Huang, Q., Liu, K., Li, Z., & Jin, B. (2014). A service brokering and recommendation mechanism for better selecting cloud services. PloS one, 9(8), e105297.
• Kersten, W., & Koch, J. (2010). The effect of quality management on the service quality and business success of logistics service providers. International Journal of Quality & Reliability Management.
• Kwon, H. K., & Seo, K. K. (2013). A decision-making model to choose a cloud service using fuzzy AHP. Advanced Science and Technology Letters, 35(1), 93-96.
• Li, A., Yang, X., Kandula, S., & Zhang, M. (2010, November). CloudCmp: comparing public cloud providers. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement (pp. 1-14).
• Ma, S. P., Lan, C. W., & Li, C. H. (2015). Contextual service discovery using term expansion and binding coverage analysis. Future Generation Computer Systems, 48, 73-81.
• Mell, P., & Grance, T. (2011). The NIST definition of cloud computing.
• Mohammadi, M., & Rezaei, J. (2020). Evaluating and comparing ontology alignment systems: An MCDM approach. Journal of Web Semantics, 64, 100592.
• Nawaz, F., Asadabadi, M. R., Janjua, N. K., Hussain, O. K., Chang, E., & Saberi, M. (2018). An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowledge-Based Systems, 159, 120-131.
• Sharabi, M. (2013). Managing and improving service quality in higher education. International Journal of Quality and Service Sciences.
• Sun, L., Dong, H., Hussain, F. K., Hussain, O. K., & Chang, E. (2014). Cloud service selection: State-of-the-art and future research directions. Journal of Network and Computer Applications, 45, 134-150.
• Zhao, C., Zhang, S., Liu, Q., Xie, J., & Hu, J. (2009, September). Independent tasks scheduling based on genetic algorithm in cloud computing. In 2009 5th international conference on wireless communications, networking and mobile computing (pp. 1-4). IEEE.
