Service Function Chaining (SFC) requires efficient placement of Virtual Network Functions (VNFs) to satisfy diverse service requirements while maintaining high resource utilization in Data Centers (DCs). Conventional static resource allocation often leads to overprovisioning or underprovisioning due to the dynamic nature of traffic loads and application demands. To address this challenge, we propose a hybrid forecast-driven Deep reinforcement learning (DRL) framework that combines predictive intelligence with SFC provisioning. Specifically, we leverage DRL to generate datasets capturing DC resource utilization and service demands, which are then used to train deep learning forecasting models. Using Optuna-based hyperparameter optimization, the best-performing models, Spatio-Temporal Graph Neural Network, Temporal Graph Neural Network, and Long Short-Term Memory, are combined into an ensemble to enhance stability and accuracy. The ensemble predictions are integrated into the DC selection process, enabling proactive placement decisions that consider both current and future resource availability. Experimental results demonstrate that the proposed method not only sustains high acceptance ratios for resource-intensive services such as Cloud Gaming and VoIP but also significantly improves acceptance ratios for latency-critical categories such as Augmented Reality increases from 30% to 50%, while Industry 4.0 improves from 30% to 45%. Consequently, the prediction-based model achieves significantly lower E2E latencies of 20.5%, 23.8%, and 34.8% reductions for VoIP, Video Streaming, and Cloud Gaming, respectively. This strategy ensures more balanced resource allocation, and reduces contention.

翻译：服务功能链（SFC）需要在数据中心（DC）中高效部署虚拟网络功能（VNF），以满足多样化的服务需求，同时保持高资源利用率。由于流量负载和应用需求的动态特性，传统的静态资源分配常导致资源过度配置或配置不足。为应对这一挑战，我们提出了一种混合预测驱动的深度强化学习（DRL）框架，将预测智能与SFC配置相结合。具体而言，我们利用DRL生成捕获DC资源利用率与服务需求的数据集，进而用于训练深度学习预测模型。通过基于Optuna的超参数优化，将性能最优的模型——时空图神经网络、时序图神经网络和长短期记忆网络——集成组合，以增强稳定性和准确性。集成预测结果被纳入DC选择过程，从而能够综合考虑当前及未来的资源可用性，做出主动的部署决策。实验结果表明，所提方法不仅能够维持对资源密集型服务（如云游戏和VoIP）的高接受率，还显著提升了时延敏感类应用的接受率：增强现实服务的接受率从30%提高至50%，工业4.0应用则从30%提升至45%。相应地，基于预测的模型在VoIP、视频流和云游戏服务上分别实现了20.5%、23.8%和34.8%的端到端时延降低。该策略确保了更均衡的资源分配，并减少了资源争用。