A Group Decision-Making Method for Selecting Cloud Computing Service Model

Al‐Jabri, Ibrahim M.; Eid, Mustafa Mahmood; Sohail, M. Sadiq

doi:10.14569/ijacsa.2018.090162

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…In some research work Multicriteria while deciding on cloud service adoption and selection has been considered [2]. Some of the research looked into the viability of using the decision-making process to choose a cloud service [3]. Many authors rambled on about different strategies for making decisions based on multiple criteria [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

Goyal,

Singh Deora

2024

IJECE

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The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

Goyal,

Singh Deora

2024

IJECE

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“…The consequences of these broad analyses demonstrate the adequacy of the proposed methodology. Ibrahim M. Al-Jabri et al [34] proposed Multi Criteria Decision Making (MCDM) based a group decision-making cloud service selection model. This model involved group of decision makers i.e.…”

Section: A General Cloud Service Selection and Ranking Solutionsmentioning

confidence: 99%

Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion Mining

Govardhanan,

Manikandan,

Arunkumar

et al. 2024

IEEE Access

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Enabling effective cloud service recommendation and ranking model helps cloud users settle on brief choices when they are up against by an enormous number of choices. This helps to improve the cloud users', as well as the service providers', satisfaction levels. In this paper, parameter reputationbased algorithms are proposed to boost the service selection and ranking. The proposed work employs prominentthe parameters of the Service Measurement Index (SMI) standardized by the Cloud Service Measurement Index Consortium (CSMIC). The parameters are systematically analyzed in order to obtain a user review opinion, such as a positive, negative and neutral view about the parameters. Here, both the positive and negative parameter reputations are addressed. The positive parameter reputation increased the cloud users' choice of service selection and ranking. The negative parameter reputation helped the service providers to realize where they are stand and give them a chance to improve their service provisioning in future endeavors. The detailed experimental results confirm that the proposed models enhance the choice of service selection and ranking.

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“…Similarly, Sahar et al [19] and Abid et al [13] have used specific QoS parameters for their evaluation and missing other parameters. Arezoo et al [10], Saurabh et al [6], Parhi et al [17], and Sahar et al [19] provide selection for one type of [30], Abid et al [13], and Ibrahim et al [2], all are providing selection for all types of services but have different limitations as mentioned in Table 3. Therefore, in view of the evaluation assessment, we infer that the proposed technique is more efficient and reliable in terms of feedback biasness, fixation of parameters and types of services.…”

Section: A Comparison and Performance Evaluationmentioning

confidence: 99%

Internet Service Selection Using Service Association Factor (SAF)

Rabbani

Aslam

2019

ITC

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The modern organizations are equipping themselves with modern current regime of online application infrastructures through cloud, fog, and edge computing. In the presence of several opportunities, the selection criterion for such Internet-based services becomes vital, especially, when there is no supporting information available. Existing recommender systems provide services by evaluating the quality of service parameters, k-mean clustering, and fuzzy logic techniques on customer's feedback. However, these schemes typically rely on customers' feedback and do not provide any information on the interrelationship between the services. Feedback may be self-generated or biased and leading to improper recommendation to service seekers. To resolve the issue, we propose an innovative service association factor method that calculates the value of interrelationships among services appearing together as a package. This technique is implemented based on an intelligent agent that evaluates the values of the associations on standards and quality attributes. It enables the users to select the best services on their preferences. The proposed agent works on fog environment near to the customers. The technique is tested on leading cloud vendors. The results show that the system meets the desires of service seekers in all service models in an efficient manner.

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A Group Decision-Making Method for Selecting Cloud Computing Service Model

Cited by 6 publications

References 23 publications

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion Mining

Internet Service Selection Using Service Association Factor (SAF)

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