Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

[1]  Gang Feng,et al.  iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks , 2019, IEEE Internet of Things Journal.

[2]  Chunlin Li,et al.  Effective replica management for improving reliability and availability in edge-cloud computing environment , 2020, J. Parallel Distributed Comput..

[3]  Chunlin Li,et al.  Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system , 2020, Inf. Sci..

[4]  Qiang Ye,et al.  SDN-Based Resource Management for Autonomous Vehicular Networks: A Multi-Access Edge Computing Approach , 2018, IEEE Wireless Communications.

[5]  Pengfei Wang,et al.  Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems , 2019, IEEE Internet of Things Journal.

[6]  Dario Pompili,et al.  Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks , 2017, IEEE Transactions on Vehicular Technology.

[7]  Liangmin Wang,et al.  A Fast Handover Scheme for SDN Based Vehicular Network , 2017, MSN.

[8]  Steven Latre,et al.  ABRAHAM: Machine Learning Backed Proactive Handover Algorithm Using SDN , 2019, IEEE Transactions on Network and Service Management.

[9]  Nick Feamster,et al.  Improving network management with software defined networking , 2013, IEEE Commun. Mag..

[10]  Danny H. K. Tsang,et al.  NOMA-Enabled Mobile Edge Computing for Internet of Things via Joint Communication and Computation Resource Allocations , 2020, IEEE Internet of Things Journal.

[11]  Atay Ozgovde,et al.  How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions , 2017, IEEE Communications Surveys & Tutorials.

[12]  Min Chen,et al.  Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network , 2018, IEEE Journal on Selected Areas in Communications.

[13]  Hongjun Dai,et al.  A scheduling algorithm for autonomous driving tasks on mobile edge computing servers , 2019, J. Syst. Archit..

[14]  Ke Zhang,et al.  Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading , 2017, IEEE Veh. Technol. Mag..

[15]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[16]  Chunlin Li,et al.  Load balance based workflow job scheduling algorithm in distributed cloud , 2020, J. Netw. Comput. Appl..

[17]  Ning Zhang,et al.  A Survey on Service Migration in Mobile Edge Computing , 2018, IEEE Access.

[18]  Chunlin Li,et al.  Mobility and marginal gain based content caching and placement for cooperative edge-cloud computing , 2021, Inf. Sci..

[19]  Koichi Adachi,et al.  Radio and Computing Resource Allocation for Minimizing Total Processing Completion Time in Mobile Edge Computing , 2019, IEEE Access.

[20]  Wei Zheng,et al.  Artificial Intelligence-Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach , 2019, IEEE Access.

[21]  Badong Chen,et al.  Efficient and robust deep learning with Correntropy-induced loss function , 2015, Neural Computing and Applications.

[22]  Lei Guo,et al.  Mobility Support for Fog Computing: An SDN Approach , 2018, IEEE Communications Magazine.

[23]  Mohsen Guizani,et al.  Mobility Management for Intro/Inter Domain Handover in Software-Defined Networks , 2019, IEEE Journal on Selected Areas in Communications.

[24]  Hongbo Zhu,et al.  Mobile Edge Cloud-Based Industrial Internet of Things: Improving Edge Intelligence With Hierarchical SDN Controllers , 2020, IEEE Vehicular Technology Magazine.

[25]  Qi Zhang,et al.  An Energy-Efficient SDN Controller Architecture for IoT Networks With Blockchain-Based Security , 2020, IEEE Transactions on Services Computing.

[26]  Roozbeh Moazenzadeh,et al.  Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature , 2019, Geoderma.

[27]  Jie Zhang,et al.  Energy-Aware Computation Offloading and Transmit Power Allocation in Ultradense IoT Networks , 2019, IEEE Internet of Things Journal.

[28]  Flavio Esposito,et al.  A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios , 2020, Sensors.

[29]  Geyong Min,et al.  Computation Offloading in Multi-Access Edge Computing Using a Deep Sequential Model Based on Reinforcement Learning , 2019, IEEE Communications Magazine.

[30]  Albert Y. Zomaya,et al.  Follow Me Fog: Toward Seamless Handover Timing Schemes in a Fog Computing Environment , 2017, IEEE Communications Magazine.

[31]  Navid Nikaein,et al.  CDS-MEC: NFV/SDN-based Application Management for MEC in 5G Systems , 2018, Comput. Networks.

[32]  Chunlin Li,et al.  An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters , 2020, Comput. Networks.

[33]  Chen Liu,et al.  Adoption of Powertrain Technologies in Automobiles—A System Dynamics Model of Technology Diffusion in the American Market , 2018, IEEE Transactions on Vehicular Technology.