Caching for Edge Inference at Scale: A Mean Field Multi-Agent Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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

Published:2023-12-04 Issue: Volume:30 Page:332-337
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Lu Yanqing¹,Zhang Meng²,Tang Ming³

Affiliation:

1. Southern University of Science and Technology,Department of Mathematics,Shenzhen,China

2. Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University,Haining,China

3. Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10436965.pdf?arnumber=10436965

Reference16 articles.

1. Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

2. EdgeAdaptor: Online Configuration Adaption, Model Selection and Resource Provisioning for Edge DNN Inference Serving at Scale

3. Collaborative Service Placement for Edge Computing in Dense Small Cell Networks

4. Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems

5. Joint Service Caching, Computation Offloading and Resource Allocation in Mobile Edge Computing Systems

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