Cost-optimized hybrid VNF deployment in edge-cloud collaborative systems via LSTM-enhanced actor-critic learning
Wenzhi Wang, Shuang Li · Open Computer Science · 2026
Abstract Edge cloud collaborative systems face critical challenges in deploying virtual network functions with dynamic adaptability and cost efficiency due to the heterogeneity of Virtual Machine-Virtual Network Function (VM-VNF) and Container Technology-Virtual Network Function (CT-VNF) resources and high environmental dynamics. This study proposes a Long Short-Term Memory Network (LSTM)-enhanced Actor-Critic framework for VNF deployment optimization. Experimental results demonstrate that the framework achieves a deployment success rate of 97.9 %, mapping success rate of 94.8 %, average latency of 362 ms, and optimal solution fitting degree of 0.93, significantly outperforming baseline methods. With average costs 30–40 % lower than comparison frameworks under varying request loads, this study provides an effective solution for service providers to implement cost-minimization management strategies in edge cloud environments. The innovation of this study lies in designing a temporally coherent dual-network architecture rather than a simple combination of existing modules. In the actor module, LSTM encodes sequential request traces to enable anticipatory deployment decisions. In the critic module, LSTM-empowered approximation is coupled with Lagrangian relaxation to evaluate long-term constraint satisfaction under mixed VM-CT deployment. This design mitigates the high variance and slow convergence inherent in conventional Actor-Critic methods under dynamic network conditions.