Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Services Computing, 2022, 16, (1), pp. 1-1
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| IEEE-TSC 2023 - Walayat.pdf | Published version | 1.47 MB |
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Due to the lack of a common framework to assess cloud providers and consumers, complicate the process of provider selection and marginal resource allocation decision. Most existing service selection and SLA management frameworks have ignored a complicated nonlinear relationship between service evaluation criteria. Due to the fact, the existing methods were unable to provide an effective decision system. These nonlinear relationships among selection criteria greatly impact the decision-making process. The paper address the critical issue by proposing a centralised Quality of Experience (QoE) and Quality of Service (QoS)- CQoES framework. The proposed system assists cloud consumers to find an optimal service provider. The framework considers the consumer's customised priority criteria, determines each criterion's relative importance, and intelligently assign relative weights to each criterion. The framework assists the service provider to manage the resources wisely and assist in decision making for marginal resources. The framework enables cloud stakeholders to build a sustainable, trusted relationship. To achieve the objective, we employ the Analytical Hierarchical Process (AHP), Induced OWA (IOWA) operator, Probabilistic OWA (POWA) operator, user-based collaborative filtering method with enhanced top KNN algorithm. The method handles complex nonlinear relationships of the selection criteria. It signifies consumer's customised criteria in relation to other criteria, then reorders inputs based on the ordered-inducing variable. The proposed method smartly unifies the provider's probabilistic information and the attitudinal characteristics for marginal resource allocation. We present two scenarios to demonstrate the approach's effectiveness and use a real cloud and other web service datasets. The experimental results show that the proposed system handles service selection and marginal resource allocation decisions.
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