Resource-Aware Dynamic Scheduling for Tasks with Deadline Constraints on Edge Computing Systems

The proliferation of various IoT devices has brought about diverse computing requests. Scheduling delay-sensitive tasks to edge nodes closer to data sources can help alleviate core network congestion and improve system quality of service (QoS). However, with the dynamic computing requirements of changing scenarios and the imbalanced performance of limited heterogeneous edge resources, resource competition among multiple tasks has become increasingly fierce. This resource competition leads to inefficient services and performance fluctuations in edge scheduling systems. The key lies in dynamically matching task requirements and limited heterogeneous resources to improve resource utilization efficiency. To overcome this challenge, we propose a resource-aware task grouping scheduling strategy (RATGS) based on our proposed group-based and sharedstate edge scheduling framework, aiming to improve the overall service quality of edge computing systems. We perform extensive evaluation on multiple metrics using realistic workloads and realworld traces. The experimental results demonstrate that RATGS improves the task completion rate by 7.56%∼50.1% before the deadline and improves the efficiency of resource utilization by 17.7%∼94.8% compared with existing baseline strategies. In addition, RATGS performed second best in terms of average completion time.