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

Xinjin Li; Siru Tao; Shihan Yin; Yujian Long; Qingze Wang; Lu Cheng; Yeyang Zhou; Calvin Chang Liu

cs.MASep 21, 2026

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

Authors: Xinjin Li, Siru Tao, Shihan Yin, Yujian Long, Qingze Wang, Lu Cheng, Yeyang Zhou, Calvin Chang Liu,

Organizations: Columbia University · Carnegie Mellon University · Brandeis University · Georgetown University ·

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.

Figures & tables

Fig. 1: Dynamic edge environment and tail-latency challenge. (A) Dynamic edge conditions with migration and bandwidth control under partial observability. (B) Uncoordinated local actions can cause load imbalance, queue explosion, and oscillation. (C) These effects increase tail latency and SLO risk at high percentiles.

Fig. 2: Overview of IMPACT. A recurrent encoder produces a latent intent from local observations and previous actions. The double-attention core then performs inter-agent message attention and intra-agent environment attention before the policy outputs coordinated bandwidth and migration actions.

Fig. 3: CTDE training and decentralized execution in IMPACT. During training, a centralized global critic provides learning signals using the global state. During execution, each agent acts with local observations, its latent intent, and selectively shared intents, while reward shaping closes the loop with the dynamic environment.

Fig. 4: Test return in 20 edges scenario.

Fig. 5: The CDF of service migration average latency in the 20-edge.

Fig. 6: Ablation Result. “e” means the loss of environment attention, “m” means the loss of message attention, and “b” means both losses.

Method	Cost $\downarrow$	Latency (ms) $\downarrow$	Deviation (%) $\downarrow$
Random	3.80	423.60	0.217
Greedy	3.40	133.30	0.088
QMIX	3.69	187.50	0.091
QPLEX	3.68	186.50	0.088
VDN	3.67	174.70	0.083
MAIC	3.66	180.90	0.087

TABLE I: Performance comparison on 20 edges. Best results are in bold. Improvements are relative to the best baseline (excluding IDDA).

Edge-Cloud Computing

Edge-cloud computing distributes data processing and AI workloads between nearby devices and remote cloud services to balance latency, cost, energy, and scale.

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Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing

Ning Yang, Chuangxin Cheng, Haijun Zhang

Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China · School of Mechano-Electronic Engineering, Xidian University, Xi’an, 710071, China · School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

Edge Computing LLM Reasoning RL for LLM Agents

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Emerging computation-intensive applications impose stringent latency requirements on resource-constrained mobile devices. Mobile Edge Computing (MEC) addresses this challenge through task offloading. However, designing effective policies remains difficult due to dynamic task arrivals, time-varying channels, and the spatio-temporal coupling of server queues. Conventional heuristics lack adaptability, while Deep Reinforcement Learning (DRL) suffers from limited generalization and architectural rigidity, requiring retraining when network topology changes. Although Large Language Models (LLMs) offer semantic reasoning capabilities, standard Supervised Fine-Tuning (SFT) yields myopic policies that greedily minimize immediate latency without accounting for long-term system evolution. To address these limitations, we propose COMLLM, a generative framework that enables foresighted decision-making in MEC systems. COMLLM integrates Group Relative Policy Optimization (GRPO) with a Look-Ahead Collaborative Simulation (LACS) mechanism, which performs multi-step Monte Carlo rollouts while jointly modeling server queue dynamics. By incorporating these rollouts into the reward design, the framework captures the long-term impact of current decisions on future system states. Experimental results demonstrate that COMLLM achieves near-optimal latency and improved load-balancing fairness. Notably, it exhibits zero-shot topological scalability, allowing a model trained on small-scale networks to generalize to larger, unseen topologies without retraining, outperforming SFT, DRL, and heuristic baselines.

Abstract

Edge-Cloud Computing

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