Caching for Edge Inference at Scale: A Mean Field Multi-Agent Reinforcement Learning Approach

Lu, Yanqing1; Zhang, Meng2; Tang, Ming3

10.1109/GLOBECOM54140.2023.10436965

2023

IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

2334-0983

2576-6813

IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM

DEC 04-08, 2023

null,Kuala Lumpur,MALAYSIA

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

To enable AI-empowered Internet-of-things (AIoT) applications, it is crucial to achieve real-time data inference (e.g., prediction, control) at network edge. However, resource-constrained Internet-of-things devices (IoTDs) may be incapable of accomplishing those computation-intensive and latency-sensitive inference tasks. To address this issue, it is promising to incorporate mobile edge computing (MEC) systems and let IoTDs offload their inference tasks to edge servers that have already cached the associated neural network model required for inference. In this work, we take into account the limited storage and computing capacity of edge servers and formulate a neural network model caching problem for an MEC system with edge inference, in order to maximize the inference accuracy and reduce the task delay. To handle the exponential growth of signaling overhead and the learning difficulty under huge number of widely-deployed edge servers, we propose a cooperative mean field multi-agent reinforcement learning framework and a mean field actor-critic algorithm to solve the aforementioned problem. Simulation results show that our proposed algorithm outperforms several benchmarks, especially in large-scale edge networks.

Mobile edge computing task offloading model caching mean field multi-agent reinforcement learning

第一 ; 通讯

英语

CPCI-S

Guangdong Basic and Applied Basic Research Foundation[2023A1515012819] ; National Natural Science Foundation of China["62202214","62202427"]

Engineering ; Telecommunications

Engineering, Electrical & Electronic ; Telecommunications

WOS:001178562000055

Web of Science

1.Southern Univ Sci & Technol, Dept Math, Shenzhen, Peoples R China
2.Zhejiang Univ, Zhejiang Univ Illinois Urbana Champaign Inst, Haining, Peoples R China
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China

Lu, Yanqing,Zhang, Meng,Tang, Ming. Caching for Edge Inference at Scale: A Mean Field Multi-Agent Reinforcement Learning Approach[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023.