Multi-Scenario Unified Resource Scheduling in Edge Cloud-Native Environment

Abstract

The development of edge computing technologies has brought about challenges in resource management. Traditional resource scheduling policies often prove insufficient due to the dynamic nature of cloud-edge collaboration. Therefore, adopting an edge cloud-native approach becomes necessary. This paper proposed a unified resource scheduling approach for joint optimization across multiple scenarios in the edge cloud-native environment. Our approach can schedule dynamically mixed-service groups across multiple scenarios by utilizing the adversarial learning of the environment and agents. Our approach can address the latency issues arising from imbalances among multiple scenarios. We conduct experiments by considering some factors such as device-number, communication-distance, CPU-cycle, and task-generation-speed. The results show that our approach can achieve a higher offloading rate and better average performance.