arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.
By Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang
arXiv:2609.17193v1 Announce Type: new
Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed...
By Zhen Li, Jun Cai, Haoran Gao, An Li, Tan Li
PPO-STGNN is a DAG task‑scheduling algorithm that combines proximal policy optimization with spatio‑temporal graph neural networks. It extracts features from both the task topology and the heterogeneous cloud‑edge‑end resource graph, then optimizes scheduling to reduce makespan and schedule length ratio while balancing CPU and memory loads. A multi‑teacher behavior‑cloning pretraining step accelerates convergence, and experiments show significant load‑balancing improvements with low completion times in dynamic, heterogeneous environments.
By Yangshuo Qi, Chenwei Wang, Zihan Shen, Songlin Sun
PPO-STGNN is a DAG task‑scheduling algorithm that combines proximal policy optimization with spatio‑temporal graph neural networks to address the NP‑hard scheduling problem in heterogeneous cloud‑edge‑end environments. It extracts features from both the DAG task topology and the physical resource graph, then optimizes the scheduling policy to minimize makespan and schedule length ratio while improving CPU and memory load balancing. A multi‑teacher behavior‑cloning pretraining step accelerates convergence, and experiments demonstrate significant load‑balancing gains with low completion times in dynamic, heterogeneous settings.
arXiv:2607. 13160v1 Announce Type: cross Abstract: Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems.
By Tianyu Pang, Hongyu Li
The paper introduces COMLLM, a generative framework that combines Group Relative Policy Optimization with a Look‑Ahead Collaborative Simulation to enable multi‑turn reasoning for task offloading in Mobile Edge Computing. By performing multi‑step Monte Carlo rollouts that jointly model server queue dynamics, COMLLM incorporates long‑term system evolution into its reward design, achieving near‑optimal latency and improved load‑balancing fairness. The framework demonstrates zero‑shot scalability to larger network topologies, outperforming supervised fine‑tuning, deep reinforcement learning, and heuristic baselines without requiring retraining.
By Ning Yang, Chuangxin Cheng, Haijun Zhang