HAMED VAHDAT-NEJAD, HOMAYUN MOTAMENI, MOHAMMAD HOSSEIN NEJAT

An integrated optimal method for cloud service ranking

Many cloud providers present various services with different attributes. It is a complex, lengthy process to select a cloud service that meets user requirements from an assortment of services. At the same time, user requirements are sometimes defined with imprecision (sets or intervals), whereas it is also important to consider the quality of user feedback (QoU) and quality of service (QoS) attributes for ranking. Besides, each MADM method has a di erent procedure, which causes functional contradictions. These contradictions have led to confusion in choosing the best MADM method. It is necessary to provide a method that harmonizes the results. Therefore, choosing a method for ranking cloud services that addresses these issues is currently a challenge. This paper proposes an optimal cloud service ranking (OCSR) method that ranks cloud services efficiently based on imprecise user requirements in both QoS and QoU aspects. OCSR consists of four stages including receiving the requirements, preprocessing, ranking, and integrating the ranking results. At the receiving requirements stage, the query format is created. In the preprocessing stage, a requirement interval is created for considering imprecise user requirements in order to filter inappropriate services. Based on QoS and QoU attributes, cloud services are then ranked through multiple multi-attribute decision-making (multi-MADM) methods such as the prominent MADM techniques. Finally, the ranking outputs of various methods are integrated to obtain the optimal results. The experimental results confirm that the OCSR outperforms the previous methods in terms of optimality of ranking, sensitivity analyses, and scalability.

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[1] Garg SK, Versteeg S, Buyya R. A framework for ranking of cloud computing services. Future Generation Computer Systems 2013; 29 (4):1012-1023.
[2] Hussain A, Chun J, Khan M. A novel customer-centric Methodology for Optimal Service Selection (MOSS) in a cloud environment. Future Generation Computer Systems 2020; 105:562-580.
[3] Jahani A, Khanli LM. Cloud service ranking as a multi objective optimization problem. The Journal of Supercomputing 2016; 72 (5):1897-1926.
[4] Lin D, Squicciarini AC, Dondapati VN, Sundareswaran S. A cloud brokerage architecture for efficient cloud service selection. IEEE Transactions on Services Computing 2016; 12 (1):144-157.
[5] Abdel-Basset M, Mohamed M, Chang V. NMCDA: A framework for evaluating cloud computing services. Future Generation Computer Systems 2018; 86:12-29.
[6] Yu P-L. Multiple-criteria decision making: concepts, techniques, and extensions. Springer Science & Business Media; 2013.
[7] Siegel J, Perdue J. Cloud services measures for global use: the service measurement index (SMI). In: 2012 Annual SRII global conference: IEEE; 2012. pp. 411-415.
[8] Iosup A, Ostermann S, Yigitbasi MN, Prodan R, Fahringer T et al. Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed systems 2011; 22 (6): 931-945.
[9] Ribas M, Furtado C, de Souza JN, Barroso GC, Moura A et al. A Petri net-based decision-making framework for assessing cloud services adoption: The use of spot instances for cost reduction. Journal of Network and Computer Applications 2015; 57:102-118.
[10] Han S-M, Hassan MM, Yoon C-W, Huh E-N. Efficient service recommendation system for cloud computing market. In: Proceedings of the 2nd international conference on interaction sciences: information technology, culture and human; 2009. pp. 839-845.
[11] Li A, Yang X, Kandula S, Zhang M. CloudCmp: comparing public cloud providers. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement; 2010. pp. 1-14.
[12] Li J, Squicciarini AC, Lin D, Sundareswaran S, Jia C. MMB cloud -Tree: Authenticated Index for Verifiable Cloud Service Selection. IEEE Transactions on Dependable and Secure computing 2015; 14 (2):185-198.
[13] Kumar RR, Mishra S, Kumar C. Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment. The Journal of Supercomputing 2017; 73 (11): 4652-4682.
[14] Sun L, Ma J, Zhang Y, Dong H, Hussain FK. Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection. Future Generation Computer Systems 2016; 57: 42-55.
[15] Sun L, Dong H, Hussain FK, Hussain OK, Chang E. Cloud service selection: State-of-the-art and future research directions. Journal of Network and Computer Applications 2014; 45:134-150.
[16] Saaty TL. Decision making with the analytic hierarchy process. International Journal of Services Sciences 2008; 1 (1): 83-98.
[17] Karim R, Ding C, Miri A. An end-to-end QoS mapping approach for cloud service selection. In: 2013 IEEE ninth world congress on services: IEEE; 2013. pp. 341-348.
[18] Opricovic S, Tzeng G-H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research 2004; 156 (2):445-455.
[19] Triantaphyllou E. Multi-criteria decision making methods. In: Multi-criteria decision making methods: A comparative study: Springer; 2000. pp. 5-21.
[20] Rezaei J, Nispeling T, Sarkis J, Tavasszy L. A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production 2016; 135: 577-588.
[21] Toinard C, Ravier T, Cérin C, Ngoko Y. The promethee method for cloud brokering with trust and assurance criteria. In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop: IEEE; 2015. pp. 1109-1118.
[22] Goraya MS, Singh D. A comparative analysis of prominently used MCDM methods in cloud environment. The Journal of Supercomputing 2021; 77 (4): 3422-3449.
[23] Tajvidi M, Ranjan R, Kolodziej J, Wang L. Fuzzy cloud service selection framework. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet): IEEE; 2014. pp. 443-448.
[24] Lee S, Seo K-K. A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy Delphi method and fuzzy AHP. Wireless Personal Communications 2016; 86 (1): 57-75.
[25] Alhanahnah M, Bertok P, Tari Z, Alouneh S. Context-aware multifaceted trust framework for evaluating trustworthiness of cloud providers. Future Generation Computer Systems 2018; 79:488-499.
[26] Bhushan SB, Pradeep RC. A network QoS aware service ranking using hybrid AHP-PROMETHEE method in multi-cloud domain. In: International Journal of Engineering Research in Africa: Trans Tech Publ; 2016. pp. 153-164.
[27] Jatoth C, Gangadharan G, Fiore U, Buyya R. SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services. Soft Computing 2019; 23 (13): 4701-4715.
[28] Jiang Y, Tao D, Liu Y, Sun J, Ling H. Cloud service recommendation based on unstructured textual information. Future Generation Computer Systems 2019; 97: 387-396.
[29] Gireesha O, Somu N, Krithivasan K, VS SS. IIVIFS-WASPAS: an integrated multi-criteria decision-making perspective for cloud service provider selection. Future Generation Computer Systems 2020; 103: 91-110.
[30] Alabool H, Kamil A, Arshad N, Alarabiat D. Cloud service evaluation method-based Multi-Criteria DecisionMaking: A systematic literature review. Journal of Systems and Software 2018; 139:161-188.
[31] Kumar U, Ahmadi A, Verma AK, Varde P. Current trends in reliability, availability, maintainability and safety: an industry perspective. Springer; 2015.
[32] Brans J-P, Vincke P. Note—A preference ranking organisation method: (The PROMETHEE method for multiple criteria decision-making). Management Science 1985; 31 (6): 647-656.
[33] Yoon K. A reconciliation among discrete compromise solutions. Journal of the Operational Research Society 1987; 38 (3): 277-286.
[34] Triantaphyllou E, Mann SH. An examination of the effectiveness of multi-dimensional decision-making methods: A decision-making paradox. Decision Support Systems 1989; 5 (3): 303-312.
[35] Christopher Frey H, Patil SR. Identification and review of sensitivity analysis methods. Risk Analysis 2002; 22 (3): 553-578.