Lyapunov-based Service Deployment Method for Fog Nodes in Intelligent Production Lines

To tackle the dynamic service deployment challenge for fog nodes in intelligent production lines, this paper presents a Lyapunov optimization-based service deployment method. Addressing the dynamic and stochastic nature of task requests in such production lines, we first formulate a deployment cost model and an optimization objective focused on maximizing the number of processed task requests. Leveraging an online Lyapunov optimization algorithm, we decompose the continuous optimization problem into single-slot online subproblems by maintaining virtual queues for deployment costs and analyzing the Lyapunov drift function. Integrated with a greedy strategy that selects deployment nodes based on minimizing the objective value, this approach achieves an optimal trade-off between deployment costs and system utility. Finally, comparative simulation experiments demonstrate that the proposed method outperforms baseline algorithms in terms of deployment cost, task failure rate, and resource utilization. This approach effectively enables dynamic and efficient service deployment on fog nodes in intelligent production lines.