Adaptive Orchestration for Inference of Large Foundation Models at the Edge

• URL: http://arxiv.org/abs/2504.03668v1
• Date: Wed, 19 Mar 2025 15:35:56 GMT
• Title: Adaptive Orchestration for Inference of Large Foundation Models at the Edge
• Authors: Fernando Koch, Aladin Djuhera, Alecio Binotto,
• Abstract summary: Large Foundation Models (LFMs) promise to unlock new capabilities for next-generation Edge AI applications.<n> performing inference with LFMs in resource-constrained and heterogeneous edge environments presents significant challenges for workload orchestration.<n>We propose a novel adaptive orchestration method and system tailored specifically for managing distributed inference workloads.
• Score: 46.1232919707345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Foundation Models (LFMs), including multi-modal and generative AI models, promise to unlock new capabilities for next-generation Edge AI applications. However, performing inference with LFMs in resource-constrained and heterogeneous edge environments presents significant challenges for workload orchestration. We propose a novel adaptive orchestration method and system tailored specifically for managing distributed inference workloads across multi-access edge computing (MEC) infrastructures. Our approach enhances traditional workload orchestration by introducing dynamic methods including: (1) adaptive workload distribution that selects optimal, inter-connected edge nodes based on runtime capacity profiling; (2) dynamic redistribution of LFM partitions as operational conditions evolve, and; (3) real-time reconfiguration (e.g., re-splitting) of LFM layers to balance performance and privacy requirements. Our proposed framework introduces an architecture for adaptive split inference, enabling real-time, QoS-aware management of inference workloads. We present a reference architecture, detail operational mechanisms, and demonstrate its application through various use cases in real-world scenarios.

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