SAMPLE: Spatiotemporal-Aware Microservice Pre-deployment with LLMs for Edge Computing

Zhixuan Wang, Shendong Gao, Yuqi Zhao, Xiulong Yang, Yatong Wang

Abstract: The quality of edge computing microservices is significantly influenced by their ability to perceive the spatiotemporal dynamics of user locations. Traditional approaches to microservice deployment in edge environments often rely on manual adjustments based on user position and base station load, which introduces substantial complexity and inefficiency. To address these challenges, we propose a novel methodology for spatiotemporal-aware microservice pre-deployment utilizing large language models (SAMPLE). By leveraging the predictive capabilities of spatiotemporal large language models, our approach enhances the microservice’s spatiotemporal awareness through trajectory forecasting. Additionally, we introduce an automated framework for generating optimal microservice deployment strategies based on the spatiotemporal relationships between users and services. Experimental results demonstrate that the proposed method significantly improves service quality by autonomously sensing user movement and dynamically adjusting deployment strategies, enhancing both the efficiency and responsiveness of edge services. The implementation code and datasets are available at https://github.com/ssea-lab/SAMPLE.

External IDs:dblp:conf/ijcnn/WangGZYW25