AdaSlicing: Adaptive Online Network Slicing under Continual Network Dynamics in Open Radio Access Networks

2025-01-12

AI Summary

To address the rigidity, isolation-efficiency trade-off, and poor adaptability of resource orchestration in dynamic Open Radio Access Network (O-RAN) environments, this paper proposes an online adaptive network slicing resource orchestration framework. Methodologically, it introduces (1) a novel soft-isolation RAN virtualization mechanism that ensures logical slice isolation while enhancing resource reuse efficiency; and (2) AdaOrch—an AI-driven orchestration algorithm integrating Bayesian learning agents for uncertainty-aware online modeling and an ADMM-based distributed coordinator for real-time responsiveness and horizontal scalability. The system is implemented atop FlexRIC, OpenAirInterface, and Open5GS, and rigorously validated on a real-world O-RAN testbed. Results demonstrate a 64.2% reduction in operational cost, a 45.5% improvement in normalized performance, and substantial gains in dynamic adaptability, service assurance, and scalability—thereby advancing practical, intelligent, and scalable O-RAN slicing orchestration.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Online Learning & Bandits
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Open radio access networks (e.g., O-RAN) facilitate fine-grained control (e.g., near-RT RIC) in next-generation networks, necessitating advanced AI/ML techniques in handling online resource orchestration in real-time. However, existing approaches can hardly adapt to time-evolving network dynamics in network slicing, leading to significant online performance degradation. In this paper, we propose AdaSlicing, a new adaptive network slicing system, to online learn to orchestrate virtual resources while efficiently adapting to continual network dynamics. The AdaSlicing system includes a new soft-isolated RAN virtualization framework and a novel AdaOrch algorithm. We design the AdaOrch algorithm by integrating AI/ML techniques (i.e., Bayesian learning agents) and optimization methods (i.e., the ADMM coordinator). We design the soft-isolated RAN virtualization to improve the virtual resource utilization of slices while assuring the isolation among virtual resources at runtime. We implement AdaSlicing on an O-RAN compliant network testbed by using OpenAirInterface RAN, Open5GS Core, and FlexRIC near-RT RIC, with Ettus USRP B210 SDR. With extensive network experiments, we demonstrate that AdaSlicing substantially outperforms state-of-the-art works with 64.2% cost reduction and 45.5% normalized performance improvement, which verifies its high adaptability, scalability, and assurance.

Problem

• O-RAN
• real-time resource management
• network performance

Innovation

• AdaSlicing
• self-adaptive network slicing
• AdaOrch algorithm