An Optimized Resource Management and Load‐Balancing Strategy for Cloud‐Assisted IoT Networks

ABSTRACT Internet of Things (IoT) devices and cloud‐based applications has introduced critical challenges in resource management and load balancing within cloud‐assisted IoT environments. This paper presents an optimized strategy integrating supervised and unsupervised machine learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid Lyrebird Falcon Optimization (HLFO), to enhance resource allocation and workload distribution across physical and virtual machines. The proposed system utilizes a multi‐objective optimization model based on key QoS metrics such as response time, availability, and throughput. Reinforcement learning further improves clustering decisions for real‐time adaptation. Simulation results demonstrate that the proposed method significantly outperforms existing models in reducing delay, minimizing packet loss, and improving throughput and packet delivery ratio, proving its effectiveness and scalability in cloud‐assisted IoT networks.