Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique

TL;DR

This paper presents a fuzzy rough set-based architecture for cloud service brokerage that ranks and selects CSPs based on user QoS requirements, improving efficiency and decision-making in multi-provider environments.

Contribution

It introduces a novel fuzzy rough set technique for dimension reduction and ranking in cloud service selection, enhancing scalability and accuracy over existing methods.

Findings

Proposed method outperforms existing ranking techniques in response time.

The approach is scalable and resilient in diverse cloud environments.

Results show improved service selection efficiency with less search time.

Abstract

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.