An Autonomic Workload Prediction and Resource Allocation Framework for Fog-Enabled Industrial IoT

Abstract

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among fog nodes. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the Deep Auto Encoder (DAE) model, and the fog nodes are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal fog node selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal fog node placement problems in efficiently executing dynamic industrial IoT workloads.