arXiv AI

PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

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.

Hugging Face Trending Papers

Sep 3

PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

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.

reinforcement-learning

arXiv Machine Learning

Jul 22

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.

By Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas

reinforcement-learning

arXiv Machine Learning

Sep 15

HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud

arXiv:2609.14968v1 Announce Type: new Abstract: Online scheduling of dependency-aware tasks in heterogeneous cloud clusters is a fundamental yet challenging problem due to the complex interplay betwe...

By Tiangang Li, Shi Ying, Xiangbo Tian

reinforcement-learning efficiency

arXiv Machine Learning

Sep 15

Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning

arXiv:2609.14952v1 Announce Type: new Abstract: Dynamic cloud workflow scheduling must balance deadline satisfaction, container utilization, and energy consumption while dealing with stochastic task-...

By Zongjin Li, Shaohan Feng, Chunxi Yang, Wenbo Wang

agents reinforcement-learning

arXiv AI

Jun 16

A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling

arXiv:2603. 23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks competing for limited heterogeneous resource pools.

By Ruisong Zhou, Haijun Zou, Li Zhou, Chumin Sun, Zaiwen Wen

reinforcement-learning

arXiv Machine Learning

Jul 8

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

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

diffusion reinforcement-learning efficiency benchmarks

arXiv AI

Aug 18

An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).

By Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low

llms rag agents reinforcement-learning

arXiv AI

Jun 2

Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts

arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.

By Ya Shen, Gang Chen, Hui Ma, Mengjie Zhang

reinforcement-learning benchmarks

arXiv Machine Learning

Aug 27

Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing

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

llms reinforcement-learning fine-tuning safety

arXiv AI

Sep 16

End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services

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

llms agents efficiency

arXiv AI

Aug 7

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application

arXiv:2608. 05346v1 Announce Type: cross Abstract: Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR).

By Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Daniel F. Macedo

agents reinforcement-learning

arXiv Machine Learning

Aug 11

ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems

arXiv:2608. 08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks.

By Zhanyu Ju, Wenchi Cheng

agents reinforcement-learning

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