This paper presents the MAPLE framework, which harnesses large language models (LLMs) to facilitate multi-agent collaboration for fully automated deployment and management of large-scale networks. Within MAPLE, a supervisor agent interprets natural language instructions from users, orchestrates specialized agents to execute tasks, and validates outcomes through integration with a network simulation platform. Experimental findings show that MAPLE outperforms single-agent approaches in terms of success rates for topology deployment and service configuration. Moreover, experiments reveal that by adaptively employing LLMs with varying capabilities according to task requirements and inter-agent dependencies, the framework effectively balances task success rates with cost efficiency.

Training distributed large language models (LLMs) for diverse user intents is challenging due to a high-dimensional, hybrid strategy space and heterogeneous workloads. We present SG2, a simulation-guided strategy generation framework that tackles intent-aware distributed LLM training by unifying Bayesian Optimization with a simulation-based evaluation loop. By leveraging near-realistic performance and intent-satisfaction metrics in simulation, SG2iteratively refines candidate training strategies before real deployment, significantly reducing exploration cost and failure risk. Experiments on heterogeneous LLM training workloads show that SG2consistently achieves higher intent-satisfaction rates and better multi-objective trade-offs than baselines, demonstrating its effectiveness for intent-aware distributed LLM training.

Deploying agentic AI services, such as large language models (LLMs) training and inference, presents significant challenges due to their complex, interdependent design across multiple layers of strategy space(framework, system, transport, network). Addressing diverse user intents with limited cross disciplinary expertise further exacerbates this complexity. To overcome these hurdles, we introduce a novel simulation-guided closed-loop (SGCL) strategy orchestration and optimization framework that is inherently intent-aware. Our approach leverages high-fidelity simulators to evaluate candidate deployment strategies, employs a surrogate-based Bayesian optimization engine to guide the closed-loop process, and incorporates a layer-wise caching mechanism to minimize redundant simulations and reduce evaluation overhead. We demonstrate its efficacy in distributed LLM training. Compared to baselines, our method consistently achieves higher intent satisfaction ratio and significantly boosts orchestration efficiency, notably reducing time-to-target by up to 94.4%. These findings highlight SGCL as a practical and effective solution for reliable, cost- and time efficient deployment of agentic AI services.

Container networks have become crucial to the overall performance and health of network systems due to the increasing adoption of container technologies. Passive per-packet and per-hop one-way delay (p4 h-OWD) measurement is essential for prompt and accurate detection and localization of network issues in container environments. Existing measurement methods for generic virtual networks require intrusive packet modification to uniquely match packets during p4 h-OWD measurements. However, the overhead and ensuing performance impact induced by high-frequency packet operations during the measurement process have been largely overlooked in the context of lightweight and weak-isolation container networks. This paper demonstrates that even state-of-the-art technologies leveraging the efficient extended Berkeley Packet Filter (eBPF) can introduce significant overhead, impacting both the networking and computing performance of container networks during p4 h-OWD measurements. To effectively reduce the p4 h-OWD measurement tax, which encompasses the measurement overhead and its consequent impact, we propose a non-intrusive method to obtain unique packet identifiers in container networks, thereby avoiding the significant operational overhead associated with intrusive packet matching. Building on this foundation, we present CNDMeas, an efficient eBPF -based technology designed for p4 h-OWD mea-surement within container networks. Evaluation results show that CNDMeas effectively limits the increase in CPU time dedicated to handling software interrupts to within 3 % and reduces the impact on both networking performance by up to 71 % in terms of delay, and computing performance during the measurement process compared to state-of-the-art technologies.