Effective Scheduling Algorithm for Workload Forecasting in Fog Environment Utilizing Dual Interactive Wasserstein Generative Adversarial Network

A decentralized computing system distributed among data-generating hardware and the cloud is called fog computing (FC). The ability to place resources to improve performance is given to users by such a flexible structure. On the other hand, low-delay services and limited resources make it difficult to use new virtualization technologies for task scheduling with resource management in fog computing. Several studies have examined scheduling and load balancing (LB) in cloud computing (CC). Nevertheless, countless LB initiatives have been proposed in fog environments. Task scheduling using a Dual interactive Wasserstein generative adversarial network (DIWGAN) optimized with an Improved Dwarf Mongoose Optimization algorithm is proposed to classify the suitable and not suitable server for the process (ESA-WF-FE-DIWGAN). Initially, the &quotCloud-Fog Computing Dataset is used. Afterward, the dataset is fed to the Fog Resource Monitor (FRM). Here, the statistical features like storage, computing, and RAM are extracted. Subsequently, the extracted features are given to DIWGAN to classify the suitable server and the unsuitable server for the process. The scheduling process was done using the Improved Dwarf Mongoose Optimization algorithm. The proposed approach achieves 3.101%, which was a 7.12% higher make span: 24.13% and 13.04% lower total cost; 2.292% and 5.365% higher ARU compared to the existing methods.