Developing Proper Systems for Successful Cloud Computing Implementation Using Fuzzy ARAS Method (Case Study: University of Tehran Faculty of New Science and Technology)

Document Type: Research Paper

Authors

1 Assistant professor, Industrial management, Faculty of management, university of Tehran, Tehran, Iran

2 Master of Science in Technology Management, University of Tehran, Tehran, Iran

3 Master of Science (MBA), University of Tehran, Tehran, Iran

4 Master of Information Technology, University of Tehran, Tehran, Iran

Abstract

Given the increasing requirements of communication and the need for advanced network-based technologies, cloud computing has been suggested as a perfect strategy to achieve these objectives. Yet, despite the development of computing applications and the increased number of alternatives, it is quite a difficult task to select the exact software platform for the implementation of cloud computing arrangements. In this line, the present paper aimed to develop a scientific framework as how to select the proper software for successful cloud computing implantation at the infrastructure level. First through a review on the related literature and using experts’ opinions, the software selection criteria were extracted. Based on the framework proposed here, the interval-valued fuzzy ARAS method was then employed for weighting and prioritizing specified alternatives. This model was applied by the Faculty of New Sciences and Technologies of Tehran University in order to select proper software platforms from among five alternatives. The results revealed that the OpenStack cloud operating system has been selected as the best alternative, most probably because this platform demonstrates significant achievement for its merits such as high level of performance, reliability and security, stability, and usability.

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خوانساری‌زاده، س.، شیرمحمدی، م. (1394). بررسی و اولویت‌بندی مخاطرات برون‎سپاری پروژه‌های فناوری اطلاعات و ارتباطات (مطالعة موردی: پروژه‌های زیرساخت فناوری اطلاعات و ارتباطات). مدیریت فناوری اطلاعات، 7(1)، 84-69.

روحانی، س.، شاه‎حسینی، م.، زارع رواسان، ا.، رحمانیان‎فر، ا. (1392). مدل انتخاب نرم‎افزار مدیریت خدمات فناوری اطلاعات مبتنی بر رویکرد تاپسیس فازی. مدیریت فناوری اطلاعات، 5(4)، 118-103.

شفایی تنکابنی، م.، شیخ، ر.، جلالی، م. (1394). پیمایشی دربارة اولویت‌بندی عوامل مؤثر بر برون‌سپاری فناوری اطلاعات در بستر رایانش ابری، در دانشگاه‌های استان سمنان با بهره‌مندی از روش دیمتل فازی. مدیریت فناوری اطلاعات، 7(2)، 344- 325.

قاسمی، ر.، محقر، ع.، صفری، ح.، اکبری جوکار، م. (1395). اولویت‌بندی کاربرهای فناوری اینترنت اشیا در بخش بهداشت و درمان ایران: محرکی برای توسعة پایدار. مدیریت فناوری اطلاعات، 8(1)، 176-155.

Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A. & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.

Aruna, L. & Aramudhan, M. (2016). Federated Architecture for Ranking the Services in Cloud Computing. Indian Journal of Science and Technology, 9(21), 1-6.

Ashtiani, B., Haghighirad, F., Makui, A. & ali Montazer, G. (2009). Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets. Applied Soft Computing, 9(2), 457-461.

Batista, D. M., Chaves, L. J., da Fonseca, N. L. & Ziviani, A. (2010). Performance analysis of available bandwidth estimation tools for grid networks. The Journal of Supercomputing, 53(1), 103-121.

Chan, H. & Chieu, T. (2010). Ranking and mapping of applications to cloud computing services by SVD. Network Operations and Management Symposium Workshops (NOMS Wksps), IEEE, 362-369.

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.

Chen, S. (1997). Fuzzy system reliability analysis based on vague set theory. IEEE International Conference on.  12-15 Oct. Orlando, FL, USA.

Chen, S. J. & Chen, S. M. (2003). Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Transactions on fuzzy systems, 11(1), 45-56.

Chen, S. J. & Chen, S. M. (2008). Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers. Computers & Mathematics with Applications, 55(8), 1670-1685.

Garg, S. K., Versteeg, S. & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems, 29(4), 1012-1023.

Garg, S.K., Versteeg, S. & Buyya, R. (2011). Smicloud: A framework for comparing and ranking cloud services. In Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on (pp. 210-218).

Gartner. (2008). G. E. Trends, Technologies Roadshow, Identifies Top Ten Disruptive Technology. Gartnet.

Ghasemi, R., Mohaghar, A., Safari, H., Akbari Jokar, M.R. (2016). Prioritizing the Applications of Internet of Things Technology in the Healthcare Sector in Iran: A Driver for Sustainable Development. Journal of Information Technology Management (JITM), 8(1), 155-176. (in Persian)

Hey, A. J., Tansley, S. & Tolle, K. M. (2009a). Microsoft Research. The fourth paradigm: data-intensive scientific discovery (Vol. 1). Redmond: WA: Microsoft research.

Hey, T., Tansley, S., & Tolle, K. M. (2009b). The fourth paradigm: data-intensive scientific discovery (Vol. 1). Redmond: WA: Microsoft research.

Khansarizadeh, S.E, Shirmohammadi, M. (2015). Investigation and Prioritizing Outsourcing of Information and Communication Technology (ICT) Projects (Case Study: ICT Infrastructure Projects). Journal of Information Technology Management (JITM), 7(1), 69-84. (in Persian)

Kuo, M. S. & Liang, G. S. (2012). A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476-485.

Kuo, M. S. (2011). A novel interval-valued fuzzy MCDM method for improving airlines service quality in Chinese cross-strait airlines. Transportation Research Part E: Logistics and Transportation Review, 47(6), 1177-1193.

Mahdavi, I., Mahdavi-Amiri, N., Heidarzade, A. & Nourifar, R. (2008). Designing a model of fuzzy TOPSIS in multiple criteria decision making. Applied Mathematics and Computation, 206(2), 607-617.

Rouhani, S., Shahhosseini, M.A., Zare Ravasan, A., Rahmanianfar, E. (2014). A Model for ITSM Software Selection using Fuzzy TOPSIS Approach. Journal of Information Technology Management (JITM), 5(4), 103-118. (in Persian)

Sakr, S., Liu, A., Batista, D. M., & Alomari, M. (2011). A survey of large scale data management approaches in cloud environments. IEEE Communications Surveys & Tutorials, 13(3), 311-336.

Saremi, M., Mousavi, S. F., & Sanayei, A. (2009). TQM consultant selection in SMEs with TOPSIS under fuzzy environment. Expert Systems with Applications, 36(2), 2742-2749.

Shafaee Tonekaboni, M.S., Sheikh, R., Jalali, M.M. (2015). Survey on the Priority Factors Influencing IT Outsourcing in the Platform of Cloud Computing in Semnan Province Universities by Fuzzy DEMATEL Technique. Journal of Information Technology Management (JITM), 7(2), 325-344. (in Persian)

Supriya, M., Sangeeta, K. & Patra, G. (2015). Comparison of AHP based and Fuzzy based mechanisms for ranking Cloud Computing services. In Computer, Control, Informatics and its Applications (IC3INA), 2015 International Conference on (pp. 175-180).

Trends, G. E. (2008). Technologies roadshow. Gartner Identifies Top Ten Disruptive Technologies for 2008 to 2012.

Tupenaite, L., Zavadskas, E., Kaklauskas, A., Turskis, Z. & Seniut, M. (2010). Multiple criteria assessment of alternatives for built and human environment renovation. Journal of Civil Engineering and Management, 16(2), 257–266.

Walterbusch, M., Martens, B. & Teuteberg, F. (2015). A decision model for the evaluation and selection of cloud computing services: A first step towards a more sustainable perspective. International Journal of Information Technology & Decision Makig, 14(2), 253-285.

Wang, M. J. & Chang, T. C. (1995). Tool steel materials selection under fuzzy environment. Fuzzy Sets and Systems, 72(3), 263-270.

Wang, Y. M. & Elhag, T. M. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert systems with applications, 31(2), 309-319.

Wei, S. H. & Chen, S. M. (2009). Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Systems with Applications, 36(2), 2285-2299.

Yao, J. S. & Lin, F. T. (2002). Constructing a fuzzy flow-shop sequencing model based on statistical data. International journal of approximate reasoning, 29(3), 215-234.

Zadeh, L. A. (1975a). The concept of a linguistic variable and its application to approximate reasoning-II. Information sciences, 8(4), 301-357.

Zadeh, L. A. (1975b). The concept of a linguistic variable and its application to approximate reasoning-III. Information sciences, 9(1), 43-80.

Zadeh, L. A. (1995). The concept of a linguistic variable and its application to approximate reasoning-I. Information sciences, 8(3), 199-249.

Zavadskas, E. & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decisionmaking. Technological and Economic Development of Economy, 16(2), 159-172.

Zavadskas, E., Turskis, Z. & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying additive ratio assessment (ARAS) method. Archives of Civil and Mechanical Engineering, 10(3), 123–141.

Zhang, Q., Cheng, L. & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1), 7-18.

Zheng, Z. & Zhang, Y. (2010). CloudRank: A QoS-driven component ranking framework for cloud computing. In Reliable Distributed Systems, 2010 29th IEEE Symposium, pp. 184-193.

Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R. & Wang, J. (2013). QoS ranking prediction for cloud services. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1213-1222.