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6G网络提供了动态电磁调控能力，但现有控制方案对快速变化的网络条件缺乏响应性，限制了其在超可靠低时延通信中的应用。本研究针对多RIS场景下具有差异化用户时延与可靠性需求的上行链路时延最小化问题，提出了符合O-RAN标准的时延感知RIS编排框架DARIO。该框架通过在短时间窗口内动态分配RIS设备给用户，自适应流量波动以满足用户级时延与可靠性目标。DARIO基于新型随机网络演算模型，在特定流量与服务动态下解析估计每种可能用户-RIS分配的时延上界。这些估计值被DARIO用于构建非线性整数规划问题，并通过在线启发式算法以较低计算开销实现近似最优性能。基于仿真与真实流量轨迹的广泛评估表明，在高负载或RIS可用性受限场景下，系统可实现高达95.7%的持续时延降低。