面向多智能体系统的流量建模：协调拓扑的作用
Towards Traffic Modelling of Multi-Agent Systems: The Role of Coordination Topology
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中文总结 AI 辅助
本研究针对多智能体LLM系统，通过500次重复运行的多层测量框架，揭示了协调拓扑对LLM请求到达过程的关键影响，相关框架与分析流程已公开。
中文摘要 AI 辅助
多智能体大语言模型（LLM）系统是一种新兴的网络化工作负载，其快速部署引发了对其产生的流量模式的疑问。与传统应用相比，这些系统会在内部生成请求：单个用户任务可诱导出结构化的模型调用序列，其时序由协调逻辑而非用户到达率决定。目前尚不清楚为人类驱动的工作负载设计的经典流量模型是否适用于该场景。我们采用多层测量框架，针对顺序、星形和全网状智能体协调拓扑，每种拓扑各进行500次重复运行，对LLM调用的到达间隔时间分布进行实证表征。我们发现，拓扑从根本上影响了LLM后端的请求到达过程：扇出协调引入了顺序执行中不存在的结构双模态，推理阶段的组件最适合用对数正态分布描述，且在所有拓扑中均果断拒绝了泊松指数零模型。这些差异会传播到推理和网络级指标。该框架和分析流程已在https URL上公开提供。
英文摘要
Multi-agent LLM systems are an emerging networked workload whose rapid deployment raises questions about the traffic patterns they generate. Compared to conventional applications, these systems generate requests internally: a single user task can induce a structured sequence of model calls whose timing is governed by coordination logic rather than by user arrival rate. It is not clear whether classical traffic models, designed for human-driven workloads, apply to this setting. We present an empirical characterisation of LLM-call interarrival time distributions across sequential, star, and full-mesh agentic coordination topologies, using a multi-layer measurement framework over 500 repeated runs per topology. We find that topology fundamentally shapes the arrival process of requests to the LLM backend: fan-out coordination introduces a structural bimodality absent in sequential execution, and the reasoningphase component is best described by a log-normal distribution, with the Poisson exponential null model decisively rejected across all topologies. These differences propagate to inference and network level metrics. The framework and analysis pipeline are released openly at https://github.com/dlamagna/agentraffic.
发表机构
- UPC, BarcelonaTech（加泰罗尼亚理工大学（巴塞罗那理工））
- Cisco（思科公司）
- Budapest University of Technology and Economics（布达佩斯技术与经济大学）
机构由 AI 辅助整理，请以论文原文为准。
