.png){kind=icon}
.png){kind=icon}
Return
AI Agent for Task Assignment in End-Edge-Cloud Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/mnet.2025.3643628.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Artificial Intelligence (AI) agents empower end-edge-cloud networks with intelligent interaction and decision-making capabilities, in which Large Language Models (LLMs) provide strong global information processing and lightweight models offer rapid responses. The integration of diverse AI agents into networks enables the exploitation of global insights for strategic decisions. However, the heterogeneous devices in end-edge-cloud networks pose significant challenges for efficient task assignment. In addition, the redundancy of global information and the limitations of local information further complicate collaborative decision-making. To this end, we propose an end-edge-cloud network with AI agents, in which LLMs and lightweight models are deployed at different layers to enable collaborative decision-making. The collaboration of diverse AI agents across multiple layers enables rapid local responses while ensuring effective extraction of global features, thereby enhancing task assignment efficiency in end-edge-cloud networks. Simulation results indicate that the collaboration between LLMs and lightweight models facilitates efficient task assignment in end-edge-cloud networks, significantly increasing the total number of completed tasks.
Keywords:
Artificial intelligence
Collaboration
Decision making
Real-time systems
Computer architecture
Servers
Cloud computing
Computational modeling
Autonomous aerial vehicles
Smart manufacturing
Foundation models
Agent-based modeling
Edge computing
Journal
IF:
6.3
Papers:
2.6K
Citations:
1.1W