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To enable an intelligent, programmable and multi-vendor radio access network(RAN) for 6G networks, considerable efforts have been made in standardizationand development of open RAN (O-RAN). So far, however, the applicability ofO-RAN in controlling and optimizing RAN functions has not been widelyinvestigated. In this paper, we jointly optimize the flow-split distribution,congestion control and scheduling (JFCS) to enable an intelligent trafficsteering application in O-RAN. Combining tools from network utilitymaximization and stochastic optimization, we introduce a multi-layeroptimization framework that provides fast convergence, long-termutility-optimality and significant delay reduction compared to thestate-of-the-art and baseline RAN approaches. Our main contributions arethree-fold: i) we propose the novel JFCS framework to efficiently andadaptively direct traffic to appropriate radio units; ii) we developlow-complexity algorithms based on the reinforcement learning, innerapproximation and bisection search methods to effectively solve the JFCSproblem in different time scales; and iii) the rigorous theoretical performanceresults are analyzed to show that there exists a scaling factor to improve thetradeoff between delay and utility-optimization. Collectively, the insights inthis work will open the door towards fully automated networks with enhancedcontrol and flexibility. Numerical results are provided to demonstrate theeffectiveness of the proposed algorithms in terms of the convergence rate,long-term utility-optimality and delay reduction.

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework