Dynamic Resource Allocation for IoT-Fog Architectures Leveraging Differential Services and Adaptive QoS Models

The constant changes in IoT-Fog architectures, allocating resources plays a vital role in overall resource management, which includes several different requests for applications. This paper proposes a dynamic resource allocation framework based on adaptive QoS and differential service models for IoT-Fog networks. Consequently, the proposed model sorts IoT applications according to the necessary QoS for their implementation, thus improving resource use and satisfying end-users. In addition, the framework complements machine learning algorithms to forecast the resources” needs and dynamically allocate the resources in response to the changing requirements, thereby increasing responsivity and decreasing resource rivalry. In this paper, to conduct several simulations and real-world evaluations of this adaptive QoS-based approach, showing that it improves service delivery, especially for latency-sensitive and high-priority applications. Thus, this research presents a method for large-scale optimization of resource allocation in the IoT-fog environment and supports reliable and efficient service deployment in distributed and often limited-resource settings.