Admission Control and Resource Provisioning in Fog-Integrated Cloud Using Modified Genetic Adaptive Neuro-Fuzzy Inference System

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.