| Title | : | DATURA: A Deep Learning–based Adaptive Traffic-aware Unified Resource Autoscaling Framework for VNFs in 5G/6G Networks |
| Speaker | : | Kanchan Kumar Tiwari (IIT Madras) |
| Details | : | Fri, 28 Nov, 2025 10:00 AM @ SSB 334 |
| Abstract: | : | This work will present DATURA, a Deep Learning(DL) based Adaptive Traffic-aware Unified Resource Auto-scaling framework for virtual network functions (VNFs) based slicing in 5G/6G networks. DATURA integrates a deep reinforcement learning (DRL) based traffic generator with a predictive GRU-MLP scaling architecture, forming a closed-loop, traffic-adaptive, and latency-aware scaling system. The framework addresses two key practical challenges that persist in existing predictive scaling solutions: the lack of realistic, dynamically varying traffic environments for evaluation, and the limited adaptability under non-stationary workloads. At the core of DATURA lies a Gated Recurrent Unit combined with a Multi-layer Perceptron (GRU-MLP) scaling controller, which forecasts per-VNF and per-slice latencies from streaming telemetry. Temporal dependencies are captured through the GRU layer, while two multitask MLP heads estimate local (per-VNF) and global (slice-level) latency trends. Once deployed, DATURA operates fully online, executing inference-driven scaling decisions in real time. The underlying models are periodically retrained with updated telemetry, enabling the framework to adapt to long-term traffic evolution without service disruption. The predicted latencies are then translated by a regression layer into proactive scaling decisions and resource allocations that maintain SLA compliance while avoiding unnecessary over-provisioning. This design replaces manual threshold tuning and online optimisation with a lightweight inference process that introduces negligible decision latency. Finally, a reactive safeguard layer ensures stability and robustness under unforeseen workload deviations. The DRL traffic generator employs Proximal Policy Optimisation (PPO) agents trained to learn transition probability matrices (TPM) that capture realistic transitions between low, medium, high, and very-high-load regimes. This enables the generation of controllable and bursty traffic traces that emulate real-world network fluctuations and stress-test scaling algorithms under reproducible conditions. The generator supports flexible composition of traffic patterns, enabling controlled mixes of load conditions that reflect realistic multi-slice behaviour and expose elasticity limits under contention. The framework is implemented in a SimPy-based 5G Core Network model for supporting UE registration and PDU session establishment, capturing control-plane dynamics. Detailed experiments for diverse traffic intensities show that DATURA consistently outperforms several existing schemes, achieving up to 73% latency reduction, 55% throughput gain, and 47% higher provisioning efficiency. These results demonstrate that DATURA provides a unified, traffic-aware, and data-driven solution for predictive and scalable VNF management in softwarised 5G/6G infrastructures. |
