This work proposes a novel learning driven bandwidth optimization framework called DRASTIC (Dynamic Resource Allocation for Slicing in Task aware Closed loop tactile Internet applications). The proposed framework dynamically allocates resources among network slices supporting both enhanced Mobile Broadband (eMBB) and high reliable low latency communication (HRLLC) users. The algorithm ensures queue stability and meets delay targets with high probability under a Markov-modulated Poisson traffic, exploiting a Lyapunov guided advantage actor critic reinforcement learning technique. The proposed network model includes an open-loop eMBB queue whose arrival and departure are mainly driven by throughput demand, as well as a closed loop HRLLC queue that captures feedback and task execution effects. A task execution dependent dexterity index adjusts the effective arrival rate, creating a feedback aware interaction between the network and the task. A probabilistic delay constraint is incorporated into the objective via Lagrangian relaxation, yielding a min_max optimization framework that enforces latency guarantees while maximizing throughput for both types of users. Simulation results demonstrate that the proposed framework meets diverse Quality of Service (QoS) requirements, maintains queue stability under dynamic wireless and robotic task variation conditions, and outperforms other approaches.

翻译：本文提出一种名为DRASTIC（面向任务感知闭环触觉互联网应用的切片动态资源分配）的新型学习驱动带宽优化框架。该框架可在同时支持增强移动宽带（eMBB）与高可靠低时延通信（HRLLC）用户的网络切片间动态分配资源。该算法利用李雅普诺夫引导的优势动作评论家强化学习技术，在马尔可夫调制的泊松流量条件下确保队列稳定性并以高概率满足时延目标。所提网络模型包含两个队列：开放环路的eMBB队列（其到达与离开主要受吞吐量需求驱动）以及闭环的HRLLC队列（可捕获反馈与任务执行效应）。任务执行依赖的灵巧度指标可调整有效到达率，从而在网络与任务间建立反馈感知交互。通过拉格朗日松弛法将概率性时延约束纳入目标函数，构建了最小-最大优化框架，该框架在强制执行时延保障的同时最大化两类用户的吞吐量。仿真结果表明，所提框架能够满足多样化的服务质量（QoS）需求，在动态无线与机器人任务变化条件下维持队列稳定性，且性能优于其他方法。