On real-time scheduling in Fog computing: A Reinforcement Learning algorithm with application to smart cities
Author:
Affiliation:
1. Sapienza University of Rome,Department of Computer, Control and Management Engineering "Antonio Ruberti"
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9766442/9767206/09767498.pdf?arnumber=9767498
Reference19 articles.
1. Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems
2. Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing
3. Neural Task Scheduling with Reinforcement Learning for Fog Computing Systems
4. A Reinforcement Learning Based Task Offloading Scheme for Vehicular Edge Computing Network
5. Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing
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1. Online Decentralized Scheduling in Fog Computing for Smart Cities Based on Reinforcement Learning;IEEE Transactions on Cognitive Communications and Networking;2024-08
2. Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM;Journal of Sensor and Actuator Networks;2024-02-01
3. A Double-Decision Reinforcement Learning Based Algorithm for Online Scheduling in Edge and Fog Computing;Algorithmic Aspects of Cloud Computing;2023-12-14
4. Fog Computing in Smart Cities: A Systematic Review;2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA);2023-10-10
5. A Load Balancing Algorithm for Equalising Latency Across Fog or Edge Computing Nodes;IEEE Transactions on Services Computing;2023-09
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