Intelligent Framework for Task Placement and Resource Allocation for IoT in the Fog-Cloud Scenario

In order to identify the latency-critical applications, this research develops an intelligent task placement and resource allocation framework (ITPRAF) that makes use of supervised feed forward neural networks. This algorithm runs through two stages. To decide whether to schedule a task for execution in the fog or send it to the cloud in the first part, this machine learning-based system uses the task's attributes as input. To ensure the best resource usage, jobs that are put to fog are reshuffled keeping into account the priority at the fog queue in the second phase. Four mechanisms for resource allocation in fog are presented in this research. Through the simulation results from various iterations, the reaction time, resource consumption, and delay satisfaction rate of each scenario were compared.