HERA: Hybrid Edge-cloud Resource Allocation for Cost-Efficient AI Agents

• URL: http://arxiv.org/abs/2504.00434v1
• Date: Tue, 01 Apr 2025 05:36:13 GMT
• Title: HERA: Hybrid Edge-cloud Resource Allocation for Cost-Efficient AI Agents
• Abstract summary: In the realm of AI, large language models (LLMs) like GPT-4, central to the operation of AI agents, predominantly operate in the cloud.<n>With local-based small language models (SLMs) becoming more accurate, the necessity of cloud-exclusive processing is being reconsidered.<n>We propose the Adaptive Iteration-level Model Selector (AIMS) to automatically partition AI agent's subtasks between local-based SLM and cloud-based LLM.
• Score: 9.064429605488026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of AI, large language models (LLMs) like GPT-4, central to the operation of AI agents, predominantly operate in the cloud, incurring high operational costs. With local-based small language models (SLMs) becoming more accurate, the necessity of cloud-exclusive processing is being reconsidered. An AI agent's response to a user's request comprises a series of subtasks or iterations. Existing approaches only allocate a single request between SLM and LLM to ensure their outputs are similar, but adopting this approach in the AI agent scenario for assigning each subtask is not effective since SLM will output a different subsequent subtask, which affects the accuracy of the final output. In this paper, we first conduct experimental analysis to understand the features of AI agent operations. Leveraging our findings, we propose the Adaptive Iteration-level Model Selector (AIMS), a lightweight scheduler to automatically partition AI agent's subtasks between local-based SLM and cloud-based LLM. AIMS considers the varying subtask features and strategically decides the location for each subtask in order to use SLM as much as possible while attaining the accuracy level. Our experimental results demonstrate that AIMS increases accuracy by up to 9.1% and SLM usage by up to 10.8% compared to HybridLLM. It offloads 45.67% of subtasks to a local SLM while attaining similar accuracy on average compared with the cloud-only LLM approach.

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