Proactive SFC Provisioning with Forecast-Driven DRL in Data Centers

2026-01-28

AI Summary

This study addresses the imbalance in resource allocation for service function chains (SFCs) caused by dynamic data center traffic and fluctuating application demands. To this end, the authors propose a novel approach that integrates deep reinforcement learning (DRL) with a multi-model ensemble predictor, uniquely incorporating an Optuna-optimized spatiotemporal graph neural network and an LSTM-based ensemble forecaster into SFC orchestration. This integration enables accurate prediction of future resource states and proactive scheduling decisions. The proposed method significantly enhances both resource utilization efficiency and quality of service: acceptance rates for augmented reality (AR) services improve from 30% to 50% (reaching 45% in Industry 4.0 scenarios), while end-to-end latency is reduced by 20.5%, 23.8%, and 34.8% for VoIP, video streaming, and cloud gaming applications, respectively.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Service Function Chaining
• Resource Allocation
• Dynamic Traffic
• Data Centers
• Overprovisioning

Innovation

• Forecast-driven DRL
• Service Function Chaining
• Ensemble forecasting
• Proactive provisioning
• Spatio-Temporal Graph Neural Network