$E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference
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Computer Science > Machine Learning
Title:$E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference
Abstract:Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time, and it is non-stationary due to user-driven semantic events, background load, and device churn. Consequently, a resource manager that is tuned offline under a fixed regime can become brittle and expensive to maintain. This paper presents $E^3$-Agent, an executable and evolving agent for edge artificial intelligence generated content (AIGC) resource management. $E^3$-Agent separates a fast-path router that makes millisecond-level dispatch decisions from a slow-path, event-driven large language model (LLM) meta-controller that mitigates regime shifts through a small, explicit control surface exposed via a tool interface, including risk gating, router configuration, and rapid performance calibration. The agent learns online from execution feedback and continuously adapts to unknown and time-varying service-time mappings. We evaluate $E^3$-Agent in a discrete-event simulator driven by MLPerf-derived device-model measurement priors, covering cold-start warmup and three dynamic regimes: semantic dynamics, device churn, and hidden drift. Across the dynamic scenarios, $E^3$-Agent reduces average latency by 65%-73% compared to the best static baseline, stays within 7%-10% of an online full-information Oracle used for evaluation, and effectively suppresses stutter rate under semantic degradation.
| Comments: | 13 pages, 4 figures, 6 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.27428 [cs.LG] |
| (or arXiv:2605.27428v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27428
arXiv-issued DOI via DataCite (pending registration)
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