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Container Placement and Migration in Edge Computing: Concept and Scheduling Models
2021
IEEE Access
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They use a Multi-Agent Deep Reinforcement Learning-based scheduling model that places a dispatching agent at each edge access point. ...
Containers are dispatched from the edge access points to the edge nodes. They modelled a multi-agent deep reinforcement learning scheduling model using MDP. The edge access point is the agent. ...
doi:10.1109/access.2021.3077550
fatcat:oy5zqgh2azbrdgfmkyuec7zor4
An Edge Server Placement Method Based on Reinforcement Learning
2022
Entropy
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In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms ...
reinforcement learning algorithm. ...
Acknowledgments: Yongjun Luo and Chunhua Gu are thanked for expert advice and inspiring discussions.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/e24030317
pmid:35327828
pmcid:PMC8946978
fatcat:qbowovokkradpdkwmzwukg3qcu
Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions
2022
Future Internet
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In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. ...
It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). ...
An energy-efficient computational offloading scheme employing deep reinforcement learning for the Intelligent Internet of Vehicles is discussed in [76] . ...
doi:10.3390/fi14070192
fatcat:knlyn5uaurarlhq7a5p66rwrgi
Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services
2021
IEEE Open Journal of the Computer Society
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Overall, multi-access edge computing can intensify the performance of delay-sensitive IoT applications compared to the core cloud based VNF deployments. ...
In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. ...
Hu. iraf: A 37 deep reinforcement learning approach for collaborative mobile edge 38 computing iot networks. IEEE Internet of Things Journal, ] H. Liao, Z. Zhou, W. Kong, Y. Chen, X. Wang, Z. ...
doi:10.1109/ojcs.2021.3098462
fatcat:mycl3xng55hnjkhczkb46axa5u
Guest Editorial Emerging Computing Offloading for IoTs: Architectures, Technologies, and Applications
2019
IEEE Internet of Things Journal
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We hope that the special issue can serve as a good reference for scientists, engineers, and academicians in the area of computation offloading in IoTs.
JIANNONG CAO ...
Special thanks are due to the Editor-in-Chief of the IEEE INTERNET OF THINGS JOURNAL, Dr. Xuemin Shen, for his help in the publication process. ...
In "Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning," the authors propose an optimal computation offloading policy by modeling offloading ...
doi:10.1109/jiot.2019.2921217
fatcat:yxc2v2whm5gtzhpefivtgt5uxy
Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey
[article]
2020
arXiv
pre-print
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This article investigates the application of machine learning techniques for in-network caching in edge networks. ...
These applications of machine learning can help identify relevant content for an edge network. ...
[50] survey the application of Deep Reinforcement Learning (DRL) towards mobile edge caching. ...
arXiv:2006.16864v4
fatcat:fwayzkv5vrghzayqxq5sgfyh2u
2020-2021 Index IEEE Transactions on Computers Vol. 70
2021
IEEE transactions on computers
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
Mukhanov, L., +, TC Nov. 2021 1976-1987 Reinforcement Learning in Edge Scenario. ...
doi:10.1109/tc.2021.3134810
fatcat:p5otlsapynbwvjmqogj47kv5qa
Table of contents
2021
IEEE transactions on intelligent transportation systems (Print)
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Farooq Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing .................................. ....................................................... A. Ndikumana, N. H. ...
Lot On the Development of an Acoustic-Driven Method to Improve Driver's Comfort Based on Deep Reinforcement Learning ............................... E. R. Nascimento, R. Bajcsy, M. Gregor, I. ...
doi:10.1109/tits.2021.3074080
fatcat:6crffein7ngyvkdxhl2dyohbgm
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
2019
IEEE Access
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A deep reinforcement learning based power control method for spectrum sharing in cognitive radios has been proposed in [110] . ...
This promising and emerging paradigm of MEC has also received a joint interest with multiple-access methods, referred to as Multi-Access Edge Computing, leveraging real-time access to the radio network ...
He is also interested in statistical computing, large-scale data mining, and analysis in earth, environmental sciences, and healthcare. Dr. ...
doi:10.1109/access.2019.2909490
fatcat:27eqrqfadfcnfmultqjnykweai
Reinforcement learning versus swarm intelligence for autonomous multi-HAPS coordination
2021
SN Applied Sciences
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AbstractThis work analyses the performance of Reinforcement Learning (RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area ...
achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. ...
via Inverse Reinforcement Learning using Deep Q-learning (ABS via IRL-DQN) by [24] ; and decentralised deep reinforcement learning algorithm [25] . ...
doi:10.1007/s42452-021-04658-6
fatcat:l4zfdhjekrbzhlt4cqrwomcihu
2021 Index IEEE Transactions on Intelligent Transportation Systems Vol. 22
2021
IEEE transactions on intelligent transportation systems (Print)
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
., +, TITS July 2021 4348-4358 Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing. ...
doi:10.1109/tits.2021.3139738
fatcat:p2mkawtrsbaepj4zk24xhyl2oa
Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0
2022
Drones
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Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. ...
We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research. ...
In addition, the authors in [57] designed a learning-based incentive system for FL using a Stackelberg game formulation and Deep Reinforcement Learning (DRL). ...
doi:10.3390/drones6070177
fatcat:533tggsjhbdjvbowfr3ku5mmci
Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks
[article]
2021
arXiv
pre-print
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In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy ...
In this context, energy-efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize 'green' 6G networks. ...
autonomous driving, unmanned aerial vehicle (UAV) aided logistics, rescue and surveillance, multi-access/mobile edge computing (MEC), and smart manufacturing [1] . ...
arXiv:2109.01287v1
fatcat:z4uk7phhpbacxebmqfoawrqmpi
AI Augmented Edge and Fog Computing: Trends and Challenges
[article]
2022
arXiv
pre-print
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We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality ...
We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. ...
We thank Shikhar Tuli, Zifeng Niu, Runan Wang, Matthew Sheldon and William Plumb for helpful discussions. ...
arXiv:2208.00761v1
fatcat:tfrhvlenyvbg7kidoydjzqejai
Cognitive Radio Networks with Reinforcement Learning Algorithms for Spectrum Allocation: A Survey
2020
International Journal of Advanced Trends in Computer Science and Engineering
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Reinforcement Learning (RL) which quickly investigates the measure of information without a model significantly encourages the exhibition of dynamic spectrum allocation in real-time application circumstances ...
The preferences and hindrances of every algorithm are investigated in their particular applications. ...
Spectrum Signals Handoff in LTE Cognitive Radio Networks Using Reinforcement Learning (2018). Spectrum Signals Handoff in LTE Cognitive Radio Networks Using Reinforcement Learning, 119-125. 21. ...
doi:10.30534/ijatcse/2020/211952020
fatcat:xbnkalqaenalvlwiob7yglhtx4
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