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Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Omogbai Oleghe
2021 IEEE Access  
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

Fei Luo, Shuai Zheng, Weichao Ding, Joel Fuentes, Yong Li
2022 Entropy  
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

Salahadin Seid Musa, Marco Zennaro, Mulugeta Libsie, Ermanno Pietrosemoli
2022 Future Internet  
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

Mahzabeen Emu, Salimur Choudhury
2021 IEEE Open Journal of the Computer Society  
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

Jiannong Cao, Deyu Zhang, Haibo Zhou, Peng-Jun Wan
2019 IEEE Internet of Things Journal  
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]

Junaid Shuja, Kashif Bilal, Waleed Alasmary, Hassan Sinky, Eisa Alanazi
2020 arXiv   pre-print
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  
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)  
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

Syed Junaid Nawaz, Shree K. Sharma, Shurjeel Wyne, Mohammad N. Patwary, Md Asaduzzaman
2019 IEEE Access  
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

Ogbonnaya Anicho, Philip B. Charlesworth, Gurvinder S. Baicher, Atulya K. Nagar
2021 SN Applied Sciences  
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)  
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

Saeed Hamood Alsamhi, Alexey V. Shvetsov, Santosh Kumar, Jahan Hassan, Mohammed A. Alhartomi, Svetlana V. Shvetsova, Radhya Sahal, Ammar Hawbani
2022 Drones  
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]

Bo Yang, Xuelin Cao, Chongwen Huang, Yong Liang Guan, Chau Yuen, Marco Di Renzo, Dusit Niyato, Merouane Debbah, Lajos Hanzo
2021 arXiv   pre-print
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]

Shreshth Tuli and Fatemeh Mirhakimi and Samodha Pallewatta and Syed Zawad and Giuliano Casale and Bahman Javadi and Feng Yan and Rajkumar Buyya and Nicholas R. Jennings
2022 arXiv   pre-print
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  
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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