IMPACT: Intent-driven Multi-agent Policy with Attention for SLO-guaranteed Microservice Migration in Cloud-edge Systems

2026-09-21

AI Summary

This study addresses the challenge of guaranteeing tail latency Service Level Objectives (SLOs) in cloud-edge systems, where user mobility, wireless fading, and partial observability pose significant difficulties. To this end, we propose an intent-driven multi-agent collaboration framework based on a centralized training with decentralized execution (CTDE) architecture to jointly optimize microservice migration and bandwidth allocation. The framework innovatively incorporates a dual-attention mechanism that aggregates peer intents while filtering local observation noise, thereby enabling efficient and robust coordination under partial observability. Experimental results demonstrate that the proposed approach reduces average latency by 30–50% and tail latency variance by 40–70%, achieving near-zero SLO violation rates with low energy consumption.

Abstract

Ensuring strict tail-latency service-level objectives (SLOs) in dynamic mobile edge computing (MEC) systems remains challenging because user mobility, wireless fading, bursty workloads, and partial observability jointly undermine reliable cloud-edge orchestration. Existing microservice migration methods predominantly optimize average delay and often decouple migration from bandwidth control, leading to uncoordinated decisions, queue oscillation, and frequent high-percentile latency violations. To address this issue, we propose IMPACT, an intent-driven Agentic AI framework for cooperative microservice migration and bandwidth control in cloud-edge systems. Under centralized training with decentralized execution (CTDE), each edge cloud is modeled as an autonomous agent that encodes local SLO risk, migration urgency, and computational pressure into compact, semantic intent representations. IMPACT further introduces a double-attention mechanism that first selectively aggregates relevant peer intents for efficient inter-agent communication and then filters local observations to emphasize goal-relevant state information. This design enables robust coordination under partial observability and jointly optimizes service migration and discrete uplink bandwidth allocation. Extensive experiments in 5-edge and 20-edge scenarios show that IMPACT reduces mean latency by 30-50% and tail-latency deviation by 40-70% compared with state-of-the-art factorized multi-agent reinforcement learning (MARL) and heuristic baselines, while achieving near-zero SLO violation rates under tight thresholds and energy consumption close to the best heuristic baseline. These results demonstrate that intent-driven agentic coordination provides an effective and scalable solution for SLO-aware orchestration in complex cloud-edge intelligent systems.

Problem

• microservice migration
• tail-latency SLO
• cloud-edge systems
• bandwidth control
• mobile edge computing

Innovation

• Intent-driven Agentic AI
• Double-Attention Mechanism
• Microservice Migration
• Multi-Agent Reinforcement Learning
• Tail-Latency SLO