A PPO-Assisted Greedy Algorithm for Link Resource Allocation in Edge-Cloud Collaboration Computing

Abstract

In edge-cloud collaborative computing architectures, when multiple edge servers transmit data to the cloud over a capacity-constrained channel, efficient link resource allocation is crucial to ensure that the cloud receives fresh information. Allocation decisions made in one time slot affect not only current performance but also future system performance, thereby necessitating foresight in the allocation algorithm. To address this challenge, we propose a proximal policy optimization (PPO)-assisted greedy algorithm (PAGA) for link resource allocation. First, based on the age of information (AoI) gain per unit link capacity in the current time slot, a greedy algorithm is employed to evaluate the transmission priorities of newly generated samples and partially transmitted samples in the buffers of sensor nodes served by each edge server. Each sample therefore obtains a greedy score that reflects only the immediate benefit within the current time slot. Subsequently, PPO captures the long-term impact of resource allocation decisions on the overall system AoI and produces a PPO-assisted score for each sample that adaptively refines the greedy evaluation. Finally, the two types of scores are fused and ranked to determine the final transmission priorities, according to which link resources are allocated to each edge server. Simulation results show that the proposed algorithm consistently achieves a lower average weighted AoI than benchmark algorithms.