Service Selection Using Multi-criteria Decision Making: A Comprehensive Overview

Hosseinzadeh, Mehdi; Hama, Hawkar Kamaran; Ghafour, Marwan Yassin; Màsdàrí, Mohàmmàd; Ahmed, Omed Hassan; Khezri, Hemn

doi:10.1007/s10922-020-09553-w

Cited by 30 publications

(17 citation statements)

References 109 publications

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“…A study explained that Multiple-Attribute Decision Making (MADM) solutions could be applied to ranking predictions and service selections due to their frequent reliance on numerous QoS factors. Resultantly, numerous management and operation science decision models can be applied to the discovery of trusted services [45]. Another study described that, before presenting a comprehensive best-service decision, the Analytic Hierarchy Process (AHP) relied on a survey form to allow various experts to assign weights to QoS criteria for services.…”

Section: Literature Reviewmentioning

confidence: 99%

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Din

Alshammari

Alanazi

et al. 2022

Sensors

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Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

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Section: Literature Reviewmentioning

confidence: 99%

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Din

Alshammari

Alanazi

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Like web service selection [47,48], MCDM methods are also popularly used for cloud service selection [49][50][51]. Youssef [52] used a combination of TOPSIS and BWM to rank cloud service providers based on nine service evaluation criteria, including sustainability, response time, usability, interoperability, cost, maintainability, reliability, scalability, and security.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

et al. 2021

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In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

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“…In contrast, SoSwirly aims to optimize only distance and number of services, but at a much larger scale. Similarly, a study by Hosseinzadeh et al [12] provides an overview of various multi-objective optimization algorithms in service networks, but in the context of selecting optimal services rather than deploying them in optimal locations. Stévant, Pazat and Blanc [13] propose a framework which monitors and optimizes service placement to minimize QoS requirements, represented by response time in their evaluation.…”

Section: Related Workmentioning

confidence: 99%

Self-organizing Fog Support Services for Responsive Edge Computing

2021

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Recent years have seen fog and edge computing emerge as new paradigms to provide more responsive software services. While both these concepts have numerous advantages in terms of efficiency and user experience by moving computational tasks closer to where they are needed, effective service scheduling requires a different approach in the geographically widespread fog than it does in the cloud. Additionally, fog and edge networks are volatile, and of such a scale that gathering all the required data for a centralized scheduler results in prohibitively high memory use and network traffic. Since the fog is a geographically distributed computational substrate, a suitable solution is to use a decentralized service scheduler, deployed on all nodes, which can monitor and deploy services in its neighbourhood without having to know the entire service topology.This article presents a fully decentralized service scheduler, labeled "SoSwirly", for fog and edge networks containing hundreds of thousands of devices. It scales service instances as required by the edge, based on available resources This is a post-peer-review, pre-copyedit version of an article published in Journal of Network and Systems Management.

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Service Selection Using Multi-criteria Decision Making: A Comprehensive Overview

Cited by 30 publications

References 109 publications

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

Self-organizing Fog Support Services for Responsive Edge Computing

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