Cloud service evaluation method-based Multi-Criteria Decision-Making: A systematic literature review

Alabool, Hamzeh; Kamil, Ahmad Syafiq; Arshad, Noreen Izza; Alarabiat, Deemah

doi:10.1016/j.jss.2018.01.038

Cited by 57 publications

(23 citation statements)

References 44 publications

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“…Then, aggregate DMs' linguistic evaluations into collective ones by Equation (11) The aggregated decision matrix is shown in Table 5. Furthermore, we convert Z-fuzzy performance values to TFNs by Equations (4) and (7). The comprehensive decision matrix X , which is combined with the performance values of quantitative criteria, is obtained as follows:…”

Section: Scale Membership Functionmentioning

confidence: 99%

“…a 1 (5,7,9) (0.7,0.9,1) (5,7,9) (0.7,0.9,1) (1,3,5) (0.3,0.5,0.7) a 2 (1,3,5) (0.9,1,1) (5,7,9) (0.7,0.9,1) (7,9,10) (0.1,0.3,0.5) a 3 (0,1,3) (0.3,0.5,0.7) (7,9,10) (0.7,0.9,1) (7,9,10) (0.3,0.5,0.7) a 4 (9,10,10) (0.5,0.7,0.9) (5,7,9) (0.5,0.7,0.9) (1,3,5) (0.7,0.9,1) DM2 a 1 (3,5,7) (0.7,0.9,1) (7,9,10) (0.9,1,1) (1,3,5) (0.3,0.5,0.7) a 2 (1,3,5) (0.7,0.9,1) (7,9,10) (0.7,0.9,1) (7,9,10) (0.3,0.5,0.7) a 3 (1,3,5) (0.3,0.5,0.7) (5,7,9) (0.7,0.9,1) (7,9,10) (0.5,0.7,0.9) a 4 (9,10,10) (0.5,0.7,0.9) (5,7,9) (0.5,0.7,0.9) (1,3,5) (0.7,0.9,1) DM3 a 1 (5,7,9) (0.7,0.9,1) (7,9,10) (0.9,1,1) (3,5,7) (0.1,0.3,0.5) a 2 (1,3,5) (0.7,0.9,1) (5,7,9) (0.7,0.9,1) (7,9,10) (0.5,0.7,0.9) a 3 (0,1,3) (0.3,0.5,0.7) (7,9,10) (0.7,0.9,1) (7,9,10) (0.5,0.7,0.9) a 4 (9,10,10) (0.3,0.5,0.7) (3,5,7) (0.7,0.9,1) (1,3,5) (0.7,0.9,1) ((1, 3, 5), (0.767, 0.933, 1)) ((5.667, 7.667, 9.333), (0.7, 0.9, 1)) ((7, 9, 10), (0.3, 0.5, 0.7)) a 3 ((0.333, 1.667, 3.667), (0.3, 0.5, 0.7)) ((6.333, 8.333, 9.667), (0.7, 0.9, 1)) ((7, 9, 10), (0.433, 0.633, 0.833)) a 4 ((9, 10, 10), (0.433, 0.633, 0.833)) ((4.333, 6.333, 8.333), (0.567, 0.767, 0.933)) ((1, 3, 5), (0.7, 0.9, 1))…”

Section: Dm1mentioning

confidence: 99%

“…Step 3. Suppose that the target value of c 1 is "Medium", i.e., (3,5,7). In addition, the target values of c 2 , c 3 , c 4 , c 5 can be computed by Equations (15) and (16).…”

Section: Dm1mentioning

confidence: 99%

See 2 more Smart Citations

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: Scale Membership Functionmentioning

confidence: 99%

Section: Dm1mentioning

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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“…e QoS-aware cloud service selection problem can be considered as a MCDM problem [13]. Multiple methods has been proposed by researchers to evaluate cloud services [4,6,14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Heterogeneous QoS-Based Cloud Service Selection Approach Using Entropy Weight and GRA-ELECTRE III

Liu

Shao

et al. 2020

Mathematical Problems in Engineering

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With the development of cloud computing, more and more resources are provided in the form of cloud services. Then how to select suitable cloud service for users without professional knowledge has become an important issue. Existing cloud service selection models are usually considered as QoS-aware evaluation focused models. In practice, the QoS attributes have problems like subjectivity, vagueness, and uncertainty, and a range of formats are involved to describe QoS attributes. Therefore, it is necessary to consider the heterogeneous formats of QoS attributes in cloud service selection process. The aim of this paper is to develop a novel cloud service selection approach using entropy weight and GRA-ELECTRE III that can handle heterogeneous QoS attributes simultaneously. In the proposed approach, heterogeneous QoS attributes are handled simultaneously by being transformed into intuitionistic fuzzy numbers; the relative weights of QoS attributes are calculated objectively by the extended entropy measure method under intuitionistic fuzzy environment; and cloud services are evaluated by GRA-ELECTRE III integrated method under intuitionistic fuzzy environment. Experimental results show that the proposed approach has good stability and discrimination in dealing with heterogeneous data and can effectively avoid compensation between attributes.

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“…Testing-as-a-Service (TaaS) based on cloud platform provides automated software testing services, saving capacity and reducing expense [1,2]. With the increasing popularity of service computing, a vast amount of services-related business applications has emerged, such as service composition [3,4], service recommendation [5][6][7][8], service evaluation [9][10][11], and service optimization [12][13][14][15]. As an important guarantee to the QoS (Quality of Service), such as test effectiveness and efficiency, service optimization has attracted much attention of researchers in software engineering.…”

Section: Introductionmentioning

confidence: 99%

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

Jiang

2018

Security and Communication Networks

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The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.

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Cloud service evaluation method-based Multi-Criteria Decision-Making: A systematic literature review

Cited by 57 publications

References 44 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

A Heterogeneous QoS-Based Cloud Service Selection Approach Using Entropy Weight and GRA-ELECTRE III

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

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