SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning

TL;DR

SafeSlice tackles the challenge of enforcing SLA-compliant, safe deep reinforcement learning for inter-slice resource allocation in O-RAN. It introduces a risk-aware, sigmoid-based reward to handle cumulative latency and a safety layer that projects actions to the nearest feasible option using a supervised latency-cost predictor. The work distinguishes instantaneous from cumulative constraints, supports SLA-threshold changes, and provides training/deployment workflows for near-real-time O-RAN operation. Empirical results with VR traffic show substantial reductions in both long-term latency and SLA violations, and improved resource efficiency, closely approaching performance with a perfect cost predictor. These findings advocate SafeSlice as a practical, robust approach for trustworthy DRL-driven network slicing in next-generation O-RAN and immersive 6G services.

Abstract

Deep reinforcement learning (DRL)-based slicing policies have shown significant success in simulated environments but face challenges in physical systems such as open radio access networks (O-RANs) due to simulation-to-reality gaps. These policies often lack safety guarantees to ensure compliance with service level agreements (SLAs), such as the strict latency requirements of immersive applications. As a result, a deployed DRL slicing agent may make resource allocation (RA) decisions that degrade system performance, particularly in previously unseen scenarios. Real-world immersive applications require maintaining SLA constraints throughout deployment to prevent risky DRL exploration. In this paper, we propose SafeSlice to address both the cumulative (trajectory-wise) and instantaneous (state-wise) latency constraints of O-RAN slices. We incorporate the cumulative constraints by designing a sigmoid-based risk-sensitive reward function that reflects the slices' latency requirements. Moreover, we build a supervised learning cost model as part of a safety layer that projects the slicing agent's RA actions to the nearest safe actions, fulfilling instantaneous constraints. We conduct an exhaustive experiment that supports multiple services, including real virtual reality (VR) gaming traffic, to investigate the performance of SafeSlice under extreme and changing deployment conditions. SafeSlice achieves reductions of up to 83.23% in average cumulative latency, 93.24% in instantaneous latency violations, and 22.13% in resource consumption compared to the baselines. The results also indicate SafeSlice's robustness to changing the threshold configurations of latency constraints, a vital deployment scenario that will be realized by the O-RAN paradigm to empower mobile network operators (MNOs).

Figures (5)

• Figure 1: SafeSlice: The proposed safe DRL-based O-RAN slicing system.
• Figure 2: Inputs and outputs of the proposed SL cost model.
• Figure 3: Cumulative latency under the four traffic test categories.
• Figure 4: Instantaneous violations accumulated over time.
• Figure 5: Resource consumption under the four test categories.