Adaptive AI Agent Placement and Migration in Edge Intelligence Systems

TL;DR

This paper introduces an adaptive framework for deploying and migrating AI agents at the edge, optimizing resource use and QoS in dynamic environments using ant colony algorithms and LLM-based optimization.

Contribution

It presents the first systematic solution for managing LLM-based AI agents in edge environments, addressing placement and migration challenges with novel algorithms.

Findings

Reduces deployment latency significantly

Lowers migration costs effectively

Improves resource utilization and QoS

Abstract

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.