2026-10-14T07:00:23Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34028154 2026-09-29T22:49:26Z category_35549 category_35678 portal_1005 item_type_14 month_year_09_2026

LLMFlowGO: An LLM-Driven System for Workflow Generation and Optimization J Xu (6794354) W Zhang (5160386) X Li (4169308) Xiao Liu (13082469) P He (13605466) J Fang (10488980) Y Yang (6099497) Information and computing sciences Data management and data science Uncategorised value As edge computing continues to expand across diverse application domains, the demand for smart applications has grown in both complexity and heterogeneity. Workflow plays a critical role in enhancing the performance and automation capability of such applications. However, traditional workflow modeling and optimization still rely on manual efforts or semi-automated tools. How to achieve automated and adaptive workflow generation while simultaneously optimizing resource allocation in edge environments remains a significant challenge. To address these issues, we propose LLMFlowGO, an LLMdriven system that integrates automated workflow generation and resource optimization in edge computing. By leveraging the semantic understanding and reasoning capabilities of large language models, LLMFlowGO automatically transforms task descriptions into structured workflows and supports service selection and configuration aligned with application constraints. Furthermore, by incorporating edge resource sensing, the system optimizes resource allocation, workflow binding, and execution management to adapt to heterogeneous environments. Experimental evaluations show that LLMFlowGO outperforms baseline methods by achieving an effective trade-off among multiple user QoS objectives without sacrificing convergence speed. LLM-FlowGO is open sourced at https://anonymous.4open.science/r/LLMFlowGo-EDE8sdafavsv1v2f, and the demo video is available at https://anonymous.4open.science/r/LLMFlowGo-EDE8sdafavsv1v2f/LLMFlowGo Demo Video.mp4.<p></p> 2026-01-01T00:00:00Z Text Conference contribution 10.26187/deakin.34028154 https://figshare.com/articles/conference_contribution/LLMFlowGO_An_LLM-Driven_System_for_Workflow_Generation_and_Optimization/34028154 All Rights Reserved Restricted Access