Network planning optimization is a fundamental problem across diverse domains, including transportation systems, communication networks, and power grids. It requires simultaneous optimization of multiple competing objectives under complex constraints. Existing network planning optimization frameworks rely on mixed integer programming (MIP) solvers, heuristics, and deep reinforcement learning (DRL) models to compute planning decisions. However, they lack effective adaptability to diverse and dynamic user intents, thus leading to the trade-off between execution time and optimality. In this paper, we propose OmniPlan, an adaptive framework that achieves both timeliness and near-optimality in network planning optimization. To achieve the adaptability lacking in existing solutions, OmniPlan employs a large language model (LLM)-based interpreter to convert heterogeneous natural-language intents into a unified and quantifiable user-preference vector. Then it employs a mixture-of-experts architecture that integrates MIP solvers, heuristics, and DRL models as specialized experts, where OmniPlan adapts to diverse intents by dynamically selecting timely and near-optimal experts. Finally, it incorporates a DRL-based expert configuration module that fine-tunes optimization objective weights to align planning decisions with user-specific preferences. We evaluate OmniPlan with a representative real-world workload, i.e., distributed machine learning (ML), where we leverage OmniPlan to offload a wide spectrum of ML inference tasks, e.g., decision trees, SVM, naive Bayes, XGBoost, and random forests, onto a network of hardware devices. Our experiments on a real-world testbed indicate that OmniPlan achieves near-optimal and low-execution-time offloading for real-world ML inference tasks, reducing latency by up to 97.8\% and network device resource consumption by up to 11.5\%.

翻译：摘要：网络规划优化是交通运输系统、通信网络及电力网络等多元领域的基础性问题，要求在复杂约束下同时优化多个相互竞争的目标。现有网络规划优化框架依赖混合整数规划求解器、启发式算法及深度强化学习模型制定规划决策，但缺乏对多样化动态用户意图的有效自适应能力，导致执行时间与最优性之间的权衡。本文提出OmniPlan，一种实现网络规划优化及时性与近最优性的自适应框架。为弥补现有方案在适应性上的不足，OmniPlan采用基于大语言模型的解释器，将异构的自然语言意图转化为统一可量化的用户偏好向量；进而采用混合专家架构，集成MIP求解器、启发式算法及DRL模型作为专用专家模块，通过动态选择及时且近最优的专家以适应多样化意图；最后引入基于DRL的专家配置模块，通过微调优化目标权重使规划决策与用户特定偏好对齐。我们以代表性真实工作负载（即分布式机器学习）评估OmniPlan，将决策树、支持向量机、朴素贝叶斯、XGBoost及随机森林等广泛的机器学习推理任务卸载至硬件设备网络。真实实验平台测试结果表明，OmniPlan实现了真实ML推理任务的近最优低时延卸载，延迟降低达97.8%，网络设备资源消耗降低达11.5%。