A user-centric federated cloud architecture for intelligent resource monitoring and QoS/QoE optimization

Abstract

Cloud federation has emerged as an effective paradigm for integrating heterogeneous cloud infrastructures to achieve scalability, interoperability, and efficient resource utilization. However, existing federated cloud systems still face significant challenges related to dynamic resource management, intelligent monitoring, interoperability, and maintaining consistent Quality of Service (QoS) while ensuring satisfactory Quality of Experience (QoE) for end users. Furthermore, most existing studies primarily focus on system-centric optimization and provide limited consideration of user-centric requirements and adaptive monitoring mechanisms. To address these limitations, this paper presents a comprehensive survey and conceptual framework for a user-centric federated cloud architecture integrating intelligent resource monitoring with QoS/QoE optimization. The study systematically analyzes federated cloud architectures, resource management strategies, monitoring techniques, security mechanisms, and QoS/QoE-aware approaches through taxonomy-based comparative analysis. Based on the identified research gaps, a multi-layered conceptual architecture is proposed consisting of User, User-Centric, Monitoring, Resource Management, Federation, and Cloud Provider layers. The proposed framework incorporates Fuzzy Inference System (FIS)-based QoE evaluation, adaptive resource allocation, intelligent monitoring, SLA tracking, and interoperability management to improve service reliability and user satisfaction. In addition, the paper highlights security, privacy, and healthcare-specific application challenges in federated cloud environments. The study concludes by identifying open research issues and future directions involving AI-driven optimization, blockchain-enabled trust management, and edge-cloud integration for next-generation federated cloud systems.