A multi-objective QoS-aware cloud service ranking approach using grey wolf optimization

Abstract

Ranking functionally similar cloud services by their quality of service (QoS) is essential for selecting the best option, yet current ranking methods generally handle only one QoS objective at a time and give up robustness once several conflicting criteria are in play. We propose a multi-objective ranking framework built on grey wolf optimization (GWO) to close this gap, pairing similarity computation via the Kendall Rank Correlation Coefficient (KRCC) with an optimization-driven prioritization step. Testing the resulting MOGWO-based method against several baseline algorithms on a cloud service dataset shows clear gains in both ranking accuracy and efficiency. The framework offers a scalable way to support decision-making in complex cloud environments and a clear structure for prioritizing services across multiple QoS dimensions at once.