CDIO: Cross-Domain Inference Optimization with Resource Preference Prediction for Edge-Cloud Collaboration
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910817087127552 |
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| author | Yang, Zheming Ji, Wen Guo, Qi Hu, Dieli Zhao, Chang Li, Xiaowei Zhao, Xuanlei Zhao, Yi Gong, Chaoyu You, Yang |
| author_facet | Yang, Zheming Ji, Wen Guo, Qi Hu, Dieli Zhao, Chang Li, Xiaowei Zhao, Xuanlei Zhao, Yi Gong, Chaoyu You, Yang |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04078 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CDIO: Cross-Domain Inference Optimization with Resource Preference Prediction for Edge-Cloud Collaboration Yang, Zheming Ji, Wen Guo, Qi Hu, Dieli Zhao, Chang Li, Xiaowei Zhao, Xuanlei Zhao, Yi Gong, Chaoyu You, Yang Multimedia 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%. |
| title | CDIO: Cross-Domain Inference Optimization with Resource Preference Prediction for Edge-Cloud Collaboration |
| topic | Multimedia |
| url | https://arxiv.org/abs/2502.04078 |