Deadline-Aware Fair Scheduling for Offloaded Tasks in Fog Computing With Inter-Fog Dependency

A fundamental problem in fog computing networks is how to schedule the deadline-aware offloaded tasks that directly arrive from the end-users and via other fog nodes. The computational resource allocation becomes more challenging when the tasks demand different delay-deadlines. In this letter, we aim to propose a scheduling strategy to maximize the number of the completed tasks within their respective deadlines while making the network strongly stable. We exploit Lyapunov drift-plus-penalty function on the queue length to schedule the tasks in the queues. Subsequently, the scheduling policy decides the amount of task to be offloaded to the underloaded fog nodes to fully utilize the computational resources offered by all fog nodes in the network. Our simulation results reveal that the proposed strategy outperforms the baseline schemes, especially when those tasks have distinct delay-deadlines.