TOWARDS HYBRID CLOUD INFRASTRUCTURE QUALITY ASSESSMENT MODEL

Abstract

The paper presents a hybrid model for the cloud infrastructure quality assessment, which combines subjective expert assessments with objective results of statistical analysis. The proposed model, called Hybrid Expert-derived with Entropy-based Weighted Sum Model (HEE-WSM), combines the Analytic Hierarchy Process (AHP) to determine weights based on expert assessments and the entropy-based approach to calculate weights using real data. The proposed HEE-WSM model is a novel approach that takes into account both expert judgments and cloud environment monitoring data. Eight criteria (such as availability, reliability, latency, scalability, performance efficiency, cost, security compliance, and support responsiveness) based on the international standards NIST SP 800-145 and ISO/IEC 25010 are proposed for the cloud infrastructure quality assessment. These criteria are divided into “benefit” and “cost” criteria, which is necessary to ensure normalization and proper comparison of different quality metrics. A hybrid mechanism for determining weighting coefficients allows balancing the weighting coefficients determined on the basis of AHP and the entropy approach using an adjustable coefficient that provides flexibility depending on decision-making needs. Thus, the flexibility of the proposed model is ensured by the ability to adjust the influence of subjective and objective weights of criteria. The final quality assessment is performed using the Weighted Sum Model that aggregates normalized quality metric scores for each alternative. To demonstrate the robustness of the proposed approach, ten leading cloud providers were analyzed in this study, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Alibaba Cloud, and several others. The obtained results demonstrated that the proposed model allows for effective evaluation of cloud services, with GCP receiving the highest total quality score. The proposed approach can be considered an adaptive, transparent, and useful tool for implementation in decision support systems for cloud infrastructure management. The proposed model can be applied in organizations and enterprises for the informed selection of cloud service providers. Future research includes the integration of real-time data monitoring and the application of machine learning methods for automatic adjustment of quality criteria weights.