Cost-AoI Aware Task Scheduling in Industrial IOT Based on Serverless Edge Computing

Wireless Industrial IoT plays a crucial role in smart factories, where many sensors are rapidly generating task requests scheduled for timely responses. Maintaining information freshness is necessary but challenging. Edge networks that combine emerging serverless feathers can enable significant improvements in development efficiency and more flexible adaptation to workloads. However, the cost of scheduling cannot be ignored. Most of the present work in serverless edge computing does not consider the impact of the age of information (AoI) and cost in task scheduling. In this paper, we consider the relationship between AoI in users and cost in service providers in practical scenarios. We model the task scheduling problem in a serverless edge computing scenario as a Markov Decision Process (MDP) and consider multi-hop forwarding task scheduling with guaranteed AoI and costs under different pressures of workloads. To solve the highly dynamic problem, we design a multi-agent deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO), validate it on real datasets, and experiments show that our algorithm reduces 10% cost in low workload and up to 16% AoI in the high workload situation.