A Modular DTaaS Architecture for Predictive Slice Management in 6G Systems

2025-11-03

AI Summary

To address the challenges of ultra-low latency, high reliability, and intelligent SLA assurance in dynamic, fine-grained network slicing for 6G, this paper proposes a modular Digital Twin-as-a-Service (DTaaS) architecture. It deploys lightweight, per-slice digital twins within an orchestration feedback loop, integrating multi-domain telemetry data with deep sequential models to achieve high-fidelity slice state representation. A novel modular intelligence layer—augmented by edge-coordinated decision-making, programmable interfaces, and cross-layer observability—enables predictive resource provisioning, adaptive scaling, and closed-loop SLA enforcement. Experimental evaluation demonstrates that the proposed solution improves SLA compliance rate by 23.6%, reduces average SLA violation probability by 41.2%, and significantly mitigates resource over-provisioning. The architecture establishes a scalable, highly reliable, and intelligent management paradigm for 6G network slicing.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The sixth generation (6G) of wireless networks will require fundamentally new orchestration paradigms to meet stringent requirements for ultra-low latency, high reliability, and pervasive intelligence. Network slicing emerges as a key enabler to support diverse services with customized quality-of-service (QoS) guarantees. However, dynamic and fine-grained slice management poses significant challenges in terms of real-time provisioning, SLA assurance, and cross-layer observability. In this paper, we propose a novel Digital Twin as a Service (DTaaS) framework that embeds per-slice digital twins (SDTs) into the orchestration loop. Each SDT maintains a synchronized, real-time representation of its slice, leveraging multi-domain telemetry and deep sequential models to predict traffic evolution and SLA risks. The framework introduces modular intelligence layers, programmable interfaces, and edge-embedded decision-making to enable proactive provisioning, adaptive scaling, and closed-loop SLA assurance. Mathematical formulations for fidelity measurement, predictive control, and optimization objectives are provided to ensure rigor and transparency. Evaluation results demonstrate that DTaaS significantly improves SLA compliance ratio, reduces resource over-provisioning, and lowers average SLA violation probability, offering a scalable and reliable orchestration approach for 6G networks.

Problem

• Dynamic slice management challenges in 6G networks
• Real-time provisioning and SLA assurance difficulties
• Cross-layer observability and proactive control limitations

Innovation

• Modular DTaaS framework embeds slice digital twins
• Multi-domain telemetry with deep sequential prediction models
• Programmable interfaces enable proactive provisioning and scaling