A Decision-Making Model for Adopting a Cloud Computing System

Yoo, Seok Keun; Kim, Bo Young

doi:10.3390/su10082952

Cited by 67 publications

(41 citation statements)

References 42 publications

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“…Step 4. (Trade-offs between criteria) In the original DNMA method, Liao & Wu [14] adjusted the criteria weights based on the mean-squared-based weighting method. In this study, we make use of the Gini-coefficient-based weighting method to replace it and extend this approach to the Z-number environment.…”

Section: A Z-number-based Dnma Methodsmentioning

confidence: 99%

“…Moreover, the CSP evaluation data often involves uncertain, unreliability, multi-scale and imprecise weights of quality of service (QoS) parameters, such as the availability, response time, and price [7]. The existing MCDM methods for CSP selection problems can be divided into three categories: the utility value-based methods such as the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) [8], VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [9,10], and Multiplicative Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) [11]; outranking methods such as the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) [12] and ELimination and Choice Expressing the Reality (ELECTRE) [13]; and preference ordering methods such as the Analytic Hierarchical Process (AHP) [14] and the Best Worst Method (BWM) [9,15,16]. The utility value-based methods are easy to understand with a ranking set as its output, and as a result are widely applied in practice [17].…”

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confidence: 99%

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Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

et al. 2020

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The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.Sustainability 2020, 12, 3410 2 of 17 future generations to meet their own needs" [2]. Sustainability requires sustainable business practices. For doing so, information systems play an important role in the organization transition towards sustainability initiatives. The sustainable information systems have been considered as an opportunity for organizations to improve productivity, reduce costs, and increase profitability [3]. The flexible, elastic and agile nature of cloud computing provides an opportunity for governmental, academic and business organizations to consider migrating their existing applications to cloud environments to make their business processes agile with minimal cost and management effort [4]. Due to the increase in the number of cloud service providers (CSPs) offering functionally similar cloud services with varying cost, features and quality, it becomes extremely complex and burdensome to select a dependable, scalable, sustainable and high cost-effective cloud service for consumers to fulfil and satisfy their requirements and business strategies. There is a pressing need of comprehensive techniques to help them for the sustainable CSP development [5,6].Sustainable CSP development can be formulated as a multicriteria decision-making (MCDM) problem [5]. It involves the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and experts for selecting the trustworthy CSPs. Moreover, the CSP evaluation data often involves uncertain, unreliability, multi-scale and imprecise weights of quality of service (QoS)...

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Section: A Z-number-based Dnma Methodsmentioning

confidence: 99%

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confidence: 99%

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

et al. 2020

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“…Marketing and Management of Innovations, 2019, Issue 3 47 http://mmi.fem.sumdu.edu.ua/en Sources: (Mell and Grance, 2011;Yoo and Kim, 2018) Cloud computing is the combination of a technology, platform that provides hosting and storage service on the Internet (Lamba and Singh, 2011). In such an environment users don't need own infrastructure for various computing services, they can be accessed from any computer in any part of the world.…”

Section: Table 1 Essential Characteristics Of Cloud Computing Characmentioning

confidence: 99%

Modelling the Efficiency of the Cloud Computing Implementation At Enterprises

Zatonatska

Dluhopolskyi

2019

MMI

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The article describes the main characteristics, types and properties of cloud computing. The most widespread cloud technologies in Ukraine are analyzed. It is identified that the largest share among users of cloud technologies in Ukraine currently belong to large holdings, IT companies, commercial enterprises and banks, but other sectors of business are also involved in the development of these services. The aim of the article is to develop the methodology for evaluating the efficiency of cloud technologies implementation at enterprises and its experimental verification. The economic component of the cloud computing implementation at enterprises (expenditures and revenues of both cloud technology owners and users) is considered. The efficiency of using cloud computing at enterprises is proved. It is found that organizations usually do not use the power of their personal data centers to a full extent. This leads to idle equipment, extra cost on maintenance and servicing of hardware, amortization, staff salaries and etc. The feasibility of transition of enterprises to cloud computing in such situations has been proved, which considerably reduce the costs of the enterprise due to the absence of need for hardware and necessary staff to support the operation of information systems. Usability of the methodology of total cost of ownership in evaluating the effectiveness of using services for the enterprise has been proved. The proposed methodology compares the main costs of using personal data centers and the cost of using cloud computing. It is experimentally proven that in most cases, the cost of maintaining personal data center (PDC) is higher than the cost of cloud services. It is also proved that the efficiency of cloud technology operation depends on the internal structure and organization of computing processes inside the systems, as well as on external factors such as the size of enterprises-clients, industries, costs for the organization of data centers, etc. Cloud computing is an advanced technology which has future prospects and is cost-effective for both enterprise users and provider organizations.

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“…Yoo and Kim analyzed critical variables in the application of cloud computing regarding technology, organization, and environment. They also pointed out top management support, competitive pressure, and compatibility to be essential factors in employing cloud computing [107].…”

Section: Cloud Computing and High-performance Computingmentioning

confidence: 99%

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

et al. 2018

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With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.

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A Decision-Making Model for Adopting a Cloud Computing System

Cited by 67 publications

References 42 publications

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

Modelling the Efficiency of the Cloud Computing Implementation At Enterprises

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

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