DRL-FORCH: A Scalable Deep Reinforcement Learning-based Fog Computing Orchestrator-Reference-Cited by-同舟云学术

DRL-FORCH: A Scalable Deep Reinforcement Learning-based Fog Computing Orchestrator

Published:2023-06-19 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 9th International Conference on Network Softwarization (NetSoft)
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Author:

Cicco Nicola Di¹,Pittalà Gaetano Francesco²,Davoli Gianluca²,Borsatti Davide²,Cerroni Walter²,Raffaelli Carla²,Tornatore Massimo¹

Affiliation:

1. Politecnico di Milano,Department of Electronics, Information, and Bioengineering (DEIB),Italy

2. University of Bologna,Department of Electrical, Electronic, and Information Engineering,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10175164/10175395/10175398.pdf?arnumber=10175398

Reference32 articles.

1. Joint optimization of caching, computing, and radio resources for fog-enabled iot using natural actor-critic deep reinforcement learning;wei;IEEE IoT Journal,2018

2. QoS-aware Task Scheduling based on Reinforcement Learning for the Cloud-Fog Continuum

3. Dynamic Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent DRL Approach

4. Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications

5. Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms;huang;Journal of Machine Learning Research,2022

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