Advanced Deep Learning Techniques for Workload Forecasting in Fog Computing, Elevating Performance Efficiency

Developing fog computing, in a nutshell, provides many practical benefits to users at the edge compared with cloud computing to address issues like inefficient resource utilization and high cost. Consequently, any reminders or urgent messages caused by unpredicted demands from users can damage the service stability and result in financial losses. Impactful allocation of resources involves predicting various tasks from embedded sources to fog networks in the fog computing. As the efficiency of predicting model is the key of the success in this area, it helps for the optimization of workload management, job distribution and the devices working state at the edge. This article introduces a feature of deep learning that relies on neural networks to filter past workload information using which, future workloads can be predicted in the fog computing environments. The evaluation of our model will be based on the results from the real datasets. certainty is reached that it will be able to accurately forecast future load patterns within its capability, by doing runnig real- time. As a result, the workforce’s primary activity will be processing data, such task is accelerate and satisfaction rate of the clients raises the most efficient ways of using the edge is also a priority.