Optimized Resource Allocation for Fog Network using Neuro-fuzzy Offloading Approach

Fog computing has emerged as one of the most important Internet infrastructures for improving service quality, particularly in real-time applications. Due to the convergence in technologies, the scope of the Internet of things (IoT) has evolved to a new dimension, it expands from data collection to device interconnections, and to pre-processing. This acceleration involves cloud and fog computing layers into the system which plays an integral role in IoT data storage and computing. Due to the diversity present in IoT devices, selection of computation devices and allocation of resources are major challenges to be addressed for efficient utilization of resources. In this paper, we presented the offloading and resource allocation model to address the solution to the above challenge. Firstly, a 5-layered neuro-fuzzy model is introduced to retrieve the fuzzy sets and rules which further passes to the fuzzy inference system to model an orchestration decision system. Additionally, to improve the system performance, we have presented the modified least loaded resource allocation algorithm which is adaptively required to reduce the failure rate of the applications. To showcase the efficacy of the model, 4 healthcare applications (augmented reality, patient pre-monitoring, record analysis, and billing systems) are evaluated with their heterogeneous parameters. The simulation findings show that our suggested model improves system performance by lowering network latency by 2.23–9.68 %, computation delay by 3.40–13.66 %, and system performance by 1.03–11.55%. The simulation results demonstrated the suggested model’s resilience in terms of network latency, computation time, and failure rate.