TY - GEN
T1 - A Hybrid Scheduling Approach for Optimizing UAV-Assisted Edge Computing toward Efficient LLM Inference
AU - Ji, Yuan
AU - Du, Xinyue
AU - Wu, Chu Ge
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Large Language Models (LLMs) play an important role in robotics and embodied intelligence system at the edge, enabling capabilities such as multi-modal data processing, real-time natural language interaction, complex task planning and autonomous decision making. However, numerous resource constrained devices cannot support the computationally intensive LLMs locally, which presents challenges in meeting quality-of-service requirements, end-to-end latency demands and bandwidth constraints for responsive operations. To address this problem, an UAV-assisted edge computing framework for LLM inference, incorporating a hybrid scheduling approach to optimize the LLM inference request scheduling and the energy consumption of the UAVs is proposed. A hybrid algorithm is proposed integrating estimation of distribution algorithm and evolution algorithms with high global exploration capabilities and efficient local exploration abilities. A series of simulation experiments is carried out to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms existing algorithms in terms of average energy consumption, stability and code complexity.
AB - Large Language Models (LLMs) play an important role in robotics and embodied intelligence system at the edge, enabling capabilities such as multi-modal data processing, real-time natural language interaction, complex task planning and autonomous decision making. However, numerous resource constrained devices cannot support the computationally intensive LLMs locally, which presents challenges in meeting quality-of-service requirements, end-to-end latency demands and bandwidth constraints for responsive operations. To address this problem, an UAV-assisted edge computing framework for LLM inference, incorporating a hybrid scheduling approach to optimize the LLM inference request scheduling and the energy consumption of the UAVs is proposed. A hybrid algorithm is proposed integrating estimation of distribution algorithm and evolution algorithms with high global exploration capabilities and efficient local exploration abilities. A series of simulation experiments is carried out to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms existing algorithms in terms of average energy consumption, stability and code complexity.
KW - estimation of distribution algorithm
KW - evolutionary algorithm
KW - large language models
KW - unmanned aerial vehicles
UR - https://www.scopus.com/pages/publications/105031890697
U2 - 10.1109/ICUS66297.2025.11295149
DO - 10.1109/ICUS66297.2025.11295149
M3 - Conference contribution
AN - SCOPUS:105031890697
T3 - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
SP - 1934
EP - 1939
BT - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
Y2 - 18 September 2025 through 19 September 2025
ER -