Reconfigurable Intelligent Surfaces (RIS) enable dynamic electromagnetic control for 6G networks, but existing control schemes lack responsiveness to fast-varying network conditions, limiting their applicability for ultra-reliable low latency communications. This work addresses uplink delay minimization in multi-RIS scenarios with heterogeneous per-user latency and reliability demands. We propose Delay-Aware RIS Orchestrator (DARIO), an O-RAN-compliant framework that dynamically assigns RIS devices to users within short time windows, adapting to traffic fluctuations to meet per-user delay and reliability targets. DARIO relies on a novel Stochastic Network Calculus (SNC) model to analytically estimate the delay bound for each possible user-RIS assignment under specific traffic and service dynamics. These estimations are used by DARIO to formulate a Nonlinear Integer Program (NIP), for which an online heuristic provides near-optimal performance with low computational overhead. Extensive evaluations with simulations and real traffic traces show consistent delay reductions up to 95.7% under high load or RIS availability.

翻译：可重构智能表面（RIS）为实现6G网络的动态电磁控制提供了可能，但现有控制方案对快速变化的网络条件缺乏响应能力，限制了其在超可靠低时延通信中的应用。本研究针对多RIS场景中具有异构化用户延迟与可靠性需求的上行链路延迟最小化问题展开。我们提出了延迟感知RIS编排器（DARIO），这是一个符合O-RAN标准的框架，能够在短时间窗口内动态为用户分配RIS设备，通过适应流量波动来满足各用户的延迟与可靠性目标。DARIO基于一种新颖的随机网络演算（SNC）模型，该模型能够在特定流量与服务动态下，解析地估计每种可能用户-RIS分配的延迟边界。DARIO利用这些估计值构建了一个非线性整数规划（NIP）问题，并通过一种在线启发式算法以较低计算开销实现了接近最优的性能。基于仿真和真实流量轨迹的广泛评估表明，在高负载或RIS可用性受限条件下，该系统能持续实现高达95.7%的延迟降低。