Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection

Hussain, Walayat; Merigó, José M.

doi:10.1007/978-3-030-78303-7_5

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…The service criteria are weighted using the full consistency method (FUCOM), whereas multi-objective optimization based on ratio analysis (MOORA) is proposed to rank CSPs. Hussain and Merigówe [32] proposed a framework for a centrally managed quality of experience and service repository. A PROMETHEE-II approach is used, in which each alternative is analyzed based on a set of custom-weighted quality of service attributes defined by the consumer.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Mostafa

2023

IEEE Access

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Cloud computing technology has undergone tremendous growth in recent years, and there are now many cloud service providers (CSPs). This makes CSP selection a challenging process for cloud users. Further complications arise when users modify the priorities of their requirements. Moreover, concerns such as complex computation, inconsistencies, and rank reversal have been raised in current approaches, resulting in less reliable results. This study presents a new hybrid multiple-criteria decision-making (MCDM) framework for ranking cloud services based on Markov chains combined with the best-only method (BOM). The Markov chain is used to record and track the changes in the priorities of user requirements and determine their final values. Then, the BOM method is utilized to determine the final weights of the QoS criteria based on pairwise comparisons made by the cloud user or decision maker and the final priorities of the user's requirements. Finally, the cloud services are ranked, and the best CSP is selected. The proposed framework was validated using a case study and a real dataset. Performance, consistency, rank conformance, and sensitivity analyses were performed to evaluate the proposed framework. The obtained results prove that the proposed framework is computationally efficient and fully consistent, considers the user requirements and its transition pattern, and is robust to rank reversal.. INDEX TERMS Cloud computing (CC), cloud service selection, QoS criteria, cloud service providers (CSPs), multiple-criteria decision-making (MCDM), best-only method (BOM), Markov chains, Rank reversal.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Mostafa

2023

IEEE Access

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“…The experimental results demonstrated high efficiency and better accuracy. The approach in [ 28 ] assists CSP selection by combining QoS and QoE.…”

Section: Introductionmentioning

confidence: 99%

Assessing cloud QoS predictions using OWA in neural network methods

Hussain

Gao

Raza

et al. 2022

Neural Comput & Applic

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Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

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“…This study evaluates the problem in terms of the multi-criteria group decision making (MCGDM) process. The MCGDM has widely used a decision-making strategy that combines multi-criteria decision making (MCDM) and group decision making approaches (Hussain and Merigó, 2022 ; Petchimuthu et al 2020 ). Naeem et al ( 2019 ) proposed a hybrid MCGDM method by combining techniques for order of preference by similarity to ideal solution (TOPSIS) and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method for stock exchange recommendations.…”

Section: Introductionmentioning

confidence: 99%

Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

Hussain

Merigó

2022

J Ambient Intell Human Comput

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There has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption.

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Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection

Cited by 8 publications

References 35 publications

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Assessing cloud QoS predictions using OWA in neural network methods

Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

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