解决AI代理在边缘计算中动态部署与迁移的难题
Adaptive AI Agent Placement and Migration in Edge Intelligence Systems
- 用蚁群算法+大模型优化，智能决定代理位置
- 仅传输关键状态，迁移成本降低显著
- 适合需要低延迟的移动AI应用开发者
以ChatGPT和Claude为代表的大型语言模型推动了对实时任务处理能力的AI代理需求。传统将数据密集型、多模态边缘工作负载迁移至云端部署的方式引入了显著延迟。将AI代理部署在边缘可提升效率并降低延迟，但边缘环境存在资源有限且异构的问题。为保障移动用户的QoS，需进行代理迁移，而复杂度高的AI代理（协调大模型、任务规划、记忆与外部工具）使迁移更加困难。本文首次提出面向动态边缘环境的基于大模型的AI代理系统性部署与管理方案。我们设计了一种新型自适应框架，建模资源约束、延迟与成本，结合蚁群算法与大模型优化实现高效决策。该方案自主放置代理以优化资源利用率与QoS，通过仅传输必要状态实现轻量级迁移。系统基于AgentScope构建，在全球分布的边缘服务器上实现并验证，显著降低了部署延迟与迁移成本。
原文摘要 · Abstract (English)
The rise of LLMs such as ChatGPT and Claude fuels the need for AI agents capable of real-time task handling. However, migrating data-intensive, multi-modal edge workloads to cloud data centers, traditionally used for agent deployment, introduces significant latency. Deploying AI agents at the edge improves efficiency and reduces latency. However, edge environments present challenges due to limited and heterogeneous resources. Maintaining QoS for mobile users necessitates agent migration, which is complicated by the complexity of AI agents coordinating LLMs, task planning, memory, and external tools. This paper presents the first systematic deployment and management solution for LLM-based AI agents in dynamic edge environments. We propose a novel adaptive framework for AI agent placement and migration in edge intelligence systems. Our approach models resource constraints and latency/cost, leveraging ant colony algorithms and LLM-based optimization for efficient decision-making. It autonomously places agents to optimize resource utilization and QoS and enables lightweight agent migration by transferring only essential state. Implemented on a distributed system using AgentScope and validated across globally distributed edge servers, our solution significantly reduces deployment latency and migration costs.
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