Prioritisation of cloud computing acceptance indicators using fuzzy AHP

Safari, Narges; Safari, Fariba; Kazemi, Mostafa; Ahmadi, Somaye; Hasanzadeh, Alireza

doi:10.1504/ijbis.2015.070206

Cited by 5 publications

(7 citation statements)

References 42 publications

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“…They derived associated metrics and stated 13 hypotheses to investigate. Then, their expert interviews have evaluated them via AHP method; all of the stated hypotheses were gotten verified except for one, which was the effect of annual IT revenue metric, out of thirteen [50]. A mathematical decision‐making model was proposed by Martens and Teuteberg [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their approach was based on fuzzy TOPSIS to rank offered services for the user business process. Safari et al [50] studied the cloud adoption determinants for SMEs in both individual and enterprise levels. They have inspired technology, organisation, and environment (TOE) framework from the literature in their proposal; then, they improved it in the individual level.…”

Section: Related Workmentioning

confidence: 99%

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A decision framework for cloud migration: A hybrid approach

2022

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Cloud computing is utilised for information technology outsourcing of either industries or organisations. There are several inhibitors and motivations related factors to determine whether one can embrace the cloud services or not. Therefore, this paper presents a holistic and flexible cloud decision framework by taking a wide spectrum weighted factors related to the acceptance or denial of the cloud services. To have sustainable decision and obviating the shortcomings of the existing approaches on the cloud service adoption, a deep understanding of organisation's business process requirements and cost implications is required. To utilise the proposed model, the functional and non-functional requirements associated to the business process of an adopter organisation must be specified. To reach a concrete decision, a hybrid approach is applied by incorporating the analytic hierarchy process and Delphi methods to prevent subjective outcomes and to have diverse experiences at the same time. To support the decision model, some economic theories and Moore law are used. To verify the proposed model, a Telecommunication Company is considered as a case study for its 6-year plan of investment. The simulation results of conducted scenarios for the mentioned mid-scale case study prove that it is logical to establish on-premises a private datacenter and utilising the hybrid deployment once it encounters abrupt burst of resource demand. Altogether, the proposed holistic model can be customised for different users with different scales.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A decision framework for cloud migration: A hybrid approach

2022

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“…The AHP is an effective and efficient decision-making technique but subjectivity of decision makers can yield uncertainties when performing pairwise comparisons. To overcome this drawback, fuzzy AHP has been used by Safari et al [67] for prioritizing cloud computing acceptance indicators. The fuzzy AHP used by Singla and Kaushal [68] for cloud path choosing of offloading in mobile cloud computing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fuzzy AHP has been used by Safari et al [67] for prioritizing cloud computing acceptance indicators, by Singla and Kaushal to allow users to select an optimal cloud service [68], by Cheng [69] for cloud computing decision making problem and a fuzzy multicriteria group decision making method based on TOPSIS technique, has been used by Wibowo et al [72] for evaluating cloud services. But these methods have some drawbacks such as: It should not represent real life situation efficiently, because it considers only the membership function and did not take into account the indeterminacy and falsity function.…”

Section: Related Work and Model Evaluationmentioning

confidence: 99%

NMCDA: A framework for evaluating cloud computing services

Abdel‐Basset

Mohamed

Chang

2018

Future Generation Computer Systems

160

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h i g h l i g h t s• Cloud services considered as a type of multi-criteria decision analysis problem. • A new approach (NMCDA) for estimating the quality of cloud services is proposed. • Ambiguous and incompatible information handling by Triangular neutrosophic numbers.• To demonstrate the performance of the proposed model a case study is presented. a b s t r a c tMany organizations are currently seeking to contract services from cloud computing rather than owing the possessions to supply those services. Due to the fast expansion of cloud computing, many cloud services have been developed. Any organization that tries to achieve the best flexibility and quick response to market requests, they have the options to use cloud services. Due to the diversity of cloud service providers, it is a very significant defy for organizations to select the appropriate cloud services which can fulfill their requirements, as numerous criteria should be counted in the selection process of cloud services. Therefore, the selection process of cloud services can be considered as a type of multi-criteria decision analysis problems. In this research paper, we present how to aid a decision maker to estimate different cloud services by providing a neutrosophic multi-criteria decision analysis (NMCDA) approach for estimating the quality of cloud services. Triangular neutrosophic numbers are used to deal with ambiguous and incompatible information which exist usually in the performance estimation process. An efficacious model is evolved depending on neutrosophic analytic hierarchy process (NAHP). The aim is to solve the performance estimation problem and improve the quality of services by creating a strong competition between cloud providers. To demonstrate the pertinence of the proposed model for disbanding the multi-criteria decision analysis, a case study is presented.The number of cloud computing services providers have increased and became a significant sector in business environments due to increasing outsourcing process of demanded technology for cloud computing services. However, the estimation process of available cloud services providers is still a complicated problem due to the following reasons [4]:1. Numerous and incompatible criteria: The evaluation criteria of cloud services providers are various and numerous, several researches listed about 100 criteria and grouped them to six main categories. These criteria are incompatible with each other in almost cases, such as achieving high performance and security of services providers apart from minimizing the operational costs. 2. Different interests of decision makers: To evaluate available cloud services providers, the evaluation criteria must be determined carefully. But the determination process of

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“…This bonanza that CC brings makes organizations and individuals concentrate more on their core businesses and competencies rather than on IT development; hence, it leads to operations being more flexible and attractive and brings agility in the organization's structure of the adopter. () On the other hand, there are several inhibitors such as lack of full IT control, vendor lock‐in, and security concerns that snag a radical shift toward cloud adoption. Clearly, there is a need for the development of decision models that evaluate the sustainability of cloud services’ competencies to cover business process requirement versus development of IT on‐premises.…”

Section: Introductionmentioning

confidence: 99%

An iterative mathematical decision model for cloud migration: A cost and security risk approach

Shirvani

Sahafi

2017

Softw Pract Exp

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This paper presents an iterative mathematical decision model for organizations to evaluate whether to invest in establishing information technology (IT) infrastructure on-premises or outsourcing IT services on a multicloud environment. This is because a single cloud cannot cover all types of users' functional/nonfunctional requirements, in addition to several drawbacks such as resource limitation, vendor lock-in, and prone to failure. On the other hand, multicloud brings several merits such as vendor lock-in avoidance, system fault tolerance, cost reduction, and better quality of service. The biggest challenge is in selecting an optimal web service composition in the ever increasing multicloud market in which each provider has its own pricing schemes and delivers variation in the service security level. In this regard, we embed a module in the cloud broker to log service downtime and different attacks to measure the security risk. If security tenets, namely, security service level agreement, such as availability, integrity, and confidentiality for mission-critical applications, are targeted by cybersecurity attacks, it causes disruption in business continuity, leading to financial losses or even business failure. To address this issue, our decision model extends the cost model by using the cost present value concept and the risk model by using the advanced mean failure cost concept, which are derived from the embedded module to quantify cloud competencies. Then, the cloud economic problem is transformed into a bioptimization problem, which minimizes cost and security risks simultaneously. To deal with the combinatorial problem, we extended a genetic algorithm to find a Pareto set of optimal solutions. To reach a concrete result and to illustrate the effectiveness of the decision model, we conducted different scenarios and a small-to-medium business IT development for a 5-year investment as a case study. The result of different implementation shows that multicloud is a promising and reliable solution against IT on-premises deployment.

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Prioritisation of cloud computing acceptance indicators using fuzzy AHP

Cited by 5 publications

References 42 publications

A decision framework for cloud migration: A hybrid approach

A decision framework for cloud migration: A hybrid approach

NMCDA: A framework for evaluating cloud computing services

An iterative mathematical decision model for cloud migration: A cost and security risk approach

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