Coordinated Communication and Computing Resource Management Using Traffic Steering and Resource Slicing in O-RAN-Based Vehicle-to-Network Communications

Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions.