A New Delay-Aware Distributed Cloud–Edge Scheduling Framework and Algorithm in Dynamic Network Environments

This paper proposes a distributed cloud–edge collaborative scheduling method to address the oversight of network transmission delay in traditional task scheduling, a critical factor that frequently leads to degraded execution efficiency. A holistic framework is introduced that dynamically models transmission delays, designs a decentralized scheduling algorithm, and optimizes resource competition through a two-dimensional matching mechanism. The framework integrates real-time network status monitoring to adjust task allocation, enabling edge nodes to independently optimize local queues and avoid single-point failures. A delay-aware scheduling algorithm is developed to balance task computing requirements and network latency, transforming three-dimensional resource matching into a two-dimensional problem to resolve conflicts in shared resource allocation. Simulation results verify that the method significantly reduces task execution time and queue backlogs compared with benchmark algorithms, demonstrating improved adaptability in dynamic network environments. This study offers a novel approach to enhancing resource utilization and system efficiency in distributed cloud–edge systems.