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arXiv · 2502.04078

CDIO: Cross-Domain Inference Optimization with Resource Preference Prediction for Edge-Cloud Collaboration

Zheming Yang·Wen Ji·Qi Guo·Dieli Hu·Chang Zhao·Xiaowei Li·Xuanlei Zhao·Yi Zhao·Chaoyu Gong·Yang You
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Abstract

Currently, massive video tasks are processed by edge-cloud collaboration. However, the diversity of task requirements and the dynamics of resources pose great challenges to efficient inference, resulting in many wasted resources. In this paper, we present CDIO, a cross-domain inference optimization framework designed for edge-cloud collaboration. For diverse input tasks, CDIO can predict resource preference types by analyzing spatial complexity and processing requirements of the task. Subsequently, a cross-domain collaborative optimization algorithm is employed to guide resource allocation in the edge-cloud system. By ensuring that each task is matched with the ideal servers, the edge-cloud system can achieve higher efficiency inference. The evaluation results on public datasets demonstrate that CDIO can effectively meet the accuracy and delay requirements for task processing. Compared to state-of-the-art edge-cloud solutions, CDIO achieves a computing and bandwidth consumption reduction of 20%-40%. And it can reduce energy consumption by more than 40%.

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Zheming Yang, Wen Ji, Qi Guo, Dieli Hu, Chang Zhao, Xiaowei Li, Xuanlei Zhao, Yi Zhao, Chaoyu Gong, Yang You. 2025-02-06. CDIO: Cross-Domain Inference Optimization with Resource Preference Prediction for Edge-Cloud Collaboration. https://arxiv.org/abs/2502.04078

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