Collaborative Model for Task Scheduling and Resource Allocation in Fog–Cloud Network Using Game Theory

The deployment of fog computing has not only helped in task offloading for the end-users toward delay-sensitive task provisioning but also reduced the burden for cloud back-end systems to process variable workloads arriving from the user equipment. However, due to the constraints on the resources and computational capabilities of the fog nodes, processing the computational-intensive task within the defined timelines is highly challenging. Also, in this scenario, offloading tasks to the cloud creates a burden on the upload link, resulting in high resource costs and delays in task processing. Existing research studies have considerably attempted to handle the task allocation problem in fog–cloud networks, but the majority of the methods are found to be computationally expensive and incur high resource costs with execution time constraints. The proposed work aims to balance resource costs and time complexity by exploring collaboration among host machines over fog nodes. It introduces the concept of task scheduling and optimal resource allocation using coalition formation methods of game theory and pay-off computation. The work also encourages the formation of coalitions among host machines to handle variable traffic efficiently. Experimental results show that the proposed approach for task scheduling and optimal resource allocation in fog computing outperforms the existing system by 56.71% in task processing time, 47.56% in unused computing resources, 8.33% in resource cost, and 37.2% in unused storage.