Cost Efficient Scheduling for Delay-Sensitive Tasks in Edge Computing System

Edge computing, as an emerging computing model, can offload delay-sensitive computing tasks from Internet of Thing (IoT) devices with limited computing resources and energy to the edge cloud. In the edge computing system, several servers are placed on the network edge near the IoT devices to process the offloaded tasks. A key issue in edge computing system is how to reduce the system cost while completing the offloaded tasks. In this paper, we study the task scheduling problem to reduce the cost of edge computing system. We model the task scheduling problem as an optimization problem, where the objective is to minimize the system cost while satisfying the delay requirements of all the tasks. Then, we prove that the proposed optimization problem is NP-hard. To solve this optimization problem effectively, we propose a task scheduling algorithm, called Two-stage Task Scheduling Cost Optimization (TTSCO). We validate the effectiveness of our algorithm by comparing with optimal solutions. The results show that the approximate ratio is less than 1.2 for 95% of the data sets we use. Performance evaluation shows that our algorithm can effectively reduce the cost of edge computing system while satisfying the delay requirements of all the tasks.