7 papers
SLICE: SLO-Driven Scheduling for LLM Inference on Edge Computing Devices
Will Chow
Large Language Models (LLMs), as the foundational architecture for next-generation interactive AI applications, not only power intelligent dialogue systems but also drive the evolu…
AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives
Haoxiang Luo, Yu Yan, Yanhui Bian +9
Artificial Intelligence (AI) techniques play a pivotal role in optimizing wireless communication networks. However, traditional deep learning approaches often act as closed boxes,…
Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Haoxiang Luo, Yinqiu Liu, Ruichen Zhang +7
Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models of…
Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training
Wenjiao Feng, Rongxing Xiao, Zonghang Li +6
Node and link churn in multi-party, cross-region clusters over wide-area networks (WANs) often disrupts distributed training. However, checkpoint-based recovery and cloud-centric a…
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks
Zhiying Wang, Gang Sun, Yuhui Wang +2
The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading. Th…
DRDST: Low-latency DAG Consensus through Robust Dynamic Sharding and Tree-broadcasting for IoV
Runhua Chen, Haoxiang Luo, Gang Sun +3
The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication e…