ABSTRACT This study introduces a novel approach for an Admission Control Manager (ACM) for allocating users requests in Fog‐integrated Cloud (FiC), based on available physical resources while ensuring Quality of Service (QoS) and Quality of Experience (QoE). The proposed ACM leverages a hybrid model combining the Genetic Algorithm (GA) and Adaptive Neuro‐Fuzzy Inference System (ANFIS), referred to as GA‐ANFIS. The GA‐ANFIS model operates in two distinct phases to address the resource provisioning challenges of the extended three‐layer FiC architecture. In the first phase, GA is employed to optimize the initial parameters of the ANFIS, ensuring better learning and convergence. In the second phase, the optimized ANFIS model processes user request parameters for job classification to decide the FiC layers for processing. The model's effectiveness is evaluated using simulations on Google trace datasets, with performance assessed via metrics such as accuracy, execution time, and convergence rate. The results demonstrate significant improvements, including a 12.63% in accuracy and a 21.66% reduction in execution time compared to state‐of‐the‐art models. These findings establish the potential of the GA‐ANFIS model as an efficient ACM to address resource provisioning challenges in FiC.

Summary Internet of Things (IoT) and other smart devices produce data that are large in volume, variety, and velocity. Cloud not only helps in data analysis, but also provides storage and computation facility to these data. It has been experienced that for many time‐critical applications, by the time request traverses back and forth to the cloud for analysis/execution, the opportunity to act upon it may get over. Therefore, time sensitivity and priority for such applications greatly matter. Fog computing, an upcoming computing infrastructure, complements the cloud and overcomes this limitation by supporting time‐sensitive and priority‐based applications by provisioning the computation, bandwidth, and storage. However, adopting fog‐integrated cloud introduces newer resource management challenges requiring a new request scheduling scheme with appropriate quality of service/experience (QoS/QoE). In this work, an intelligent admission control manager is being proposed for placing the request based on the parameters such as CPU, memory, storage besides few other categorical parameters, for example, job priority and time sensitivity. The proposed work applies machine intelligence techniques, clustering for labeling the applications' requests followed by a decision tree, using the labeled requests, to classify the incoming requests. The proposed methodology is demonstrated in terms of accuracy, execution time, precision, recall, variation in accuracy, and execution time by introducing noise in multiple size batches to avoid the generalization error and fault tolerance. A comparative study with few well‐known classifiers has also been performed to ascertain the effectiveness of the proposed model. The proposed model is light enough to be placed appropriately on the fog node.