QMADM-W: A Hybrid MADM Framework for Cloud Service Selection with Unavailable Data

Abstract

The rapid expansion of cloud computing has made it increasingly difficult for users to determine the most appropriate cloud service provider (CSP). The provider offers diverse services, typically assessed based on quality of service (QoS) attributes, including throughput, reliability, availability, latency, and response time. Researchers often present these QoS attributes in a decision matrix and apply multi-attribute decision-making (MADM) algorithms to evaluate and rank the CSPs. However, in practical scenarios, not all CSPs satisfy every QoS attribute, leading to unavailable performance measure values in a decision matrix. To address this challenge, we develop a hybrid MADM framework for CSP selection that handles an incomplete decision matrix. The framework integrates QoS-aware MADM (QMADM) algorithms, QTOPSISW and QVIKOR-W with attribute weights (QMADM-W). It employs three imputation techniques to determine unavailable performance values: minimum (min), maximum (max), and mean. The weights are derived using the analytic hierarchy process (AHP) and the analytic network process (ANP). Simulation results using the QoS for web services (QWS) dataset demonstrate the framework’s effectiveness in QTPOSIS-W, with consistent and robust performance observed under the mean imputation technique through sensitivity analysis. The proposed algorithms offer a reliable solution for selecting an optimal CSP, even for an incomplete decision matrix.