A Unified Framework for User-Preferred Multi-Level Ranking of Cloud Computing Services Based on Usability and Quality of Service Evaluation

• Main Author: Farrukh Nadeem
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Cloud computing services evaluation and analysis; cloud services ranking; cloud services attributes and key performance indicators
• Online Access: https://ieeexplore.ieee.org/document/9208735/

As networking and Cloud computing technologies have evolved, a wide range of Cloud services has been introduced by different Cloud service providers. Many organizations and individuals are using these services as a part of their regular work. Thus, the performance of users' systems is significantly dependent upon the performance of the services they employ. Therefore, it becomes crucial for Cloud users to thoroughly evaluate and compare the available Cloud services to select the best of them. However, different Cloud service models, range of pricing and feature schemes, different performance attributes used by service providers, fuzzy nature of some of the performance attributes, etc. make Cloud service performance analysis and comparison a challenging task. In addition, different user preferences regarding Cloud service attributes make this analysis even more complex. This situation leads to ambiguity and indecisiveness in selecting a Cloud service that best matches the end-user's needs and thus leads to degraded performance of user's systems and financial losses. To this end, this article proposes a unified Cloud service measurement index to provide a single comprehensive framework for multi-level evaluation of Cloud services. For a detailed and effective performance evaluation, we identified 8 top-level attributes of Cloud services and 65 detailed key performance indicators to evaluate these attributes. For an analytical ranking of the target Cloud services, we employed “Multi-Attribute Global Inference of Quality”, which considers the hierarchical relationship of performance attributes. Our method considers user preferences for Cloud service attributes in terms of attribute weights and is flexible to select all or only user-preferred Cloud service attributes. We show the application of the proposed framework and the ranking method using a case study.