A Multi-Objective Optimization Framework for QoS-Driven Service and Channel Allocation in Dynamic Wireless Computer Network Environments

Efficient resource allocation in large-scale networking environments is critical for ensuring optimal Quality of Service (QoS) across diverse applications. This study proposes an innovative multi-objective optimization model for dynamic service and channel allocation, addressing key QoS parameters such as latency, energy consumption, and cost. The model employs a weight-based optimization framework, enabling real-time adaptive resource assignment while balancing trade-offs among QoS constraints, including channel capacity and service demand. The proposed model was evaluated using MATLAB simulations, demonstrating an 18% improvement in load balancing, a 22% reduction in latency, and a 15% improvement in energy efficiency. The numerical results validate the effectiveness of the proposed approach in high-demand scenarios, showcasing improvements in network utilization and seamless service delivery. This study establishes a scalable framework for QoS-driven resource allocation in 5G networks, IoT systems, and cloud computing environments, ensuring optimal service performance under dynamic conditions.