Title: QMADM-S: data-driven algorithms for enhancing cloud service decision-making through imputation

Authors: P. Navya; Sanjaya Kumar Panda; Rashmi Ranjan Rout

Addresses: Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal – 506004, Telangana, India ' Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal – 506004, Telangana, India ' Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal – 506004, Telangana, India

Abstract: With the rapid advancement of cloud computing, users encounter difficulties in selecting the best cloud service providers (CSPs). These providers offer numerous services, evaluated based on key quality of service (QoS) attributes such as availability, latency, reliability, response time, and throughput. Multi-attribute decision-making (MADM) techniques are widely used to assess CSP performance. However, in real-world applications, unavailable QoS values often result in incomplete decision matrices, potentially affecting the accuracy of CSP rankings. This study evaluates the impact of three imputation techniques, min, max, and mean, on handling unavailable performance data across different levels of unavailability in seven QoS MADM with Spearman's rank correlation coefficient (QMADM-S) algorithms to assess ranking consistency. The analysis uses the QoS for web services (QWS) dataset and sensitivity analysis to identify the most effective imputation technique. Simulation results indicate that the mean imputation technique maintains ranking stability better than the other imputation techniques.

Keywords: cloud service provider; CSP; multi-attribute decision-making; quality of service; QoS; unavailable performance measure value; imputation technique; spearman's rank correlation coefficient; sensitivity analysis.

DOI: 10.1504/IJWGS.2025.147117

International Journal of Web and Grid Services, 2025 Vol.21 No.2, pp.163 - 198

Received: 12 Mar 2025
Accepted: 13 May 2025
Published online: 10 Jul 2025 *

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