Towards Efficient Edge Computing Through Adoption of Reinforcement Learning Strategies: A Review
- 22 September 2022
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 41 references indexed in Scilit:
- Foundations and Evolution of Modern Computing Paradigms: Cloud, IoT, Edge, and FogIEEE Access, 2019
- Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and NetworksIEEE Transactions on Vehicular Technology, 2019
- Deep Reinforcement Learning Based VNF Management in Geo-distributed Edge ComputingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- Online Deep Reinforcement Learning for Computation Offloading in Blockchain-Empowered Mobile Edge ComputingIEEE Transactions on Vehicular Technology, 2019
- Deep Reinforcement Learning for IoT Network Dynamic Clustering in Edge ComputingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT NetworksIEEE Internet of Things Journal, 2019
- Research on Reinforcement Learning-Based Dynamic Power Management for Edge Data CenterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge ComputingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Deep Reinforcement Learning based Resource Allocation in Low Latency Edge Computing NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Edge Computing: Vision and ChallengesIEEE Internet of Things Journal, 2016