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Decentralized adaptive resource-aware computation offloading & caching for multi-access edge computing networks

Tefera, Getenet, She, Kun, Shelke, Maya, Ahmed, Awais·2021·Sustainable Computing: Informatics and Systems
DOI 10.1016/j.suscom.2021.100555
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Research works printed but not linked: 39

  1. Chen, 2015, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE/ACM Trans. Netw., 24, 2795, 10.1109/TNET.2015.2487344 · DOI · Paper page
  2. Chen, 2014, Decentralized computation offloading game for mobile cloud computing, IEEE Trans. Parallel Distrib. Syst., 26, 974, 10.1109/TPDS.2014.2316834 · DOI
  3. Chen, 2018, Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning, IEEE Internet Things J., 6, 4005, 10.1109/JIOT.2018.2876279 · DOI · Paper page
  4. Fooladivanda, 2012, Joint resource allocation and user association for heterogeneous wireless cellular networks, IEEE Trans. Wireless Commun., 12, 248, 10.1109/TWC.2012.121112.120018 · DOI
  5. He, 2017, Integrated networking, caching, and computing for connected vehicles: a deep reinforcement learning approach, IEEE Trans. Veh. Technol., 67, 44, 10.1109/TVT.2017.2760281 · DOI · Paper page
  6. El-Sayed, 2017, Edge of things: the big picture on the integration of edge, IoT and the cloud in a distributed computing environment, IEEE Access, 6, 1706, 10.1109/ACCESS.2017.2780087 · DOI
  7. Cao, 2019, Intelligent offloading in multi-access edge computing: a state-of-the-art review and framework, IEEE Commun. Mag., 57, 56, 10.1109/MCOM.2019.1800608 · DOI
  8. Zheng, 2018, Dynamic computation offloading for mobile cloud computing: a stochastic game-theoretic approach, IEEE Trans. Mob. Comput., 18, 771, 10.1109/TMC.2018.2847337 · DOI
  9. Cao, 2017, Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: a game-theoretic machine learning approach, IEEE Trans. Veh. Technol., 67, 752, 10.1109/TVT.2017.2740724 · DOI
  10. Min, 2019, Learning-based computation offloading for IoT devices with energy harvesting, IEEE Trans. Veh. Technol., 68, 1930, 10.1109/TVT.2018.2890685 · DOI · Paper page
  11. Hu, 2018, Mobility-aware edge caching and computing in vehicle networks: a deep reinforcement learning, IEEE Trans. Veh. Technol., 67, 10190, 10.1109/TVT.2018.2867191 · DOI · Paper page
  12. Wang, 2018, Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications, IEEE Trans. Ind. Inf., 15, 976, 10.1109/TII.2018.2883991 · DOI
  13. Bouet, 2018, Mobile edge computing resources optimization: a geo-clustering approach, IEEE Trans. Netw. Serv. Manage., 15, 787, 10.1109/TNSM.2018.2816263 · DOI
  14. Yu, 2018, Computation offloading with data caching enhancement for mobile edge computing, IEEE Trans. Veh. Technol., 67, 11098, 10.1109/TVT.2018.2869144 · DOI
  15. Taleb, 2017, On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration, IEEE Commun. Surv. Tutorials, 19, 1657, 10.1109/COMST.2017.2705720 · DOI · Paper page
  16. Alameddine, 2019, Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing, IEEE J. Sel. Areas Commun., 37, 668, 10.1109/JSAC.2019.2894306 · DOI
  17. Guo, 2018, Collaborative computation offloading for multiaccess edge computing over fiber–wireless networks, IEEE Trans. Veh. Technol., 67, 4514, 10.1109/TVT.2018.2790421 · DOI
  18. Moura, 2018, Game theory for multi-access edge computing: survey, use cases, and future trends, IEEE Commun. Surv. Tutorials, 21, 260, 10.1109/COMST.2018.2863030 · DOI · Paper page
  19. Wang, 2019, Smart resource allocation for mobile edge computing: a deep reinforcement learning approach, IEEE Trans. Emerging Top. Comput.
  20. Wei, 2018, Dynamic edge computation offloading for internet of things with energy harvesting: a learning method, IEEE Internet Things J., 6, 4436, 10.1109/JIOT.2018.2882783 · DOI
  21. Asheralieva, 2019, Hierarchical game-theoretic and reinforcement learning framework for computational offloading in UAV-enabled mobile edge computing networks with multiple service providers, IEEE Internet Things J., 6, 8753, 10.1109/JIOT.2019.2923702 · DOI
  22. Qi, 2019, Knowledge-driven service offloading decision for vehicular edge computing: a deep reinforcement learning approach, IEEE Trans. Veh. Technol., 68, 4192, 10.1109/TVT.2019.2894437 · DOI
  23. Dinh, 2018, Learning for computation offloading in mobile edge computing, IEEE Trans. Commun., 66, 6353, 10.1109/TCOMM.2018.2866572 · DOI · Paper page
  24. Liu, 2017, eNB selection for machine type communications using reinforcement learning based Markov decision process, IEEE Trans. Veh. Technol., 66, 11330, 10.1109/TVT.2017.2730230 · DOI
  25. Tefera, 2020, Congestion-aware adaptive decentralised computation offloading and caching for multi-access edge computing networks, IET Commun., 14, 3410, 10.1049/iet-com.2020.0630 · DOI
  26. Qiu, 2019, Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing, IEEE Trans. Veh. Technol., 68, 8050, 10.1109/TVT.2019.2924015 · DOI
  27. Munir, 2019, When edge computing meets microgrid: a deep reinforcement learning approach, IEEE Internet Things J., 6, 7360, 10.1109/JIOT.2019.2899673 · DOI
  28. Wang, 2019, Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning, IEEE Commun. Mag., 57, 64, 10.1109/MCOM.2019.1800971 · DOI
  29. Cheng, 2018, Localized small cell caching: a machine learning approach based on rating data, IEEE Trans. Commun., 67, 1663, 10.1109/TCOMM.2018.2878231 · DOI
  30. Yang, 2018, Multi-access edge computing enhanced video streaming: proof-of-concept implementation and prediction/QoE models, IEEE Trans. Veh. Technol., 68, 1888, 10.1109/TVT.2018.2889196 · DOI
  31. Lien, 2018, Low latency radio access in 3GPP local area data networks for V2X: stochastic optimization and learning, IEEE Internet Things J., 6, 4867, 10.1109/JIOT.2018.2874883 · DOI
  32. Ning, 2019, Deep reinforcement learning for intelligent internet of vehicles: an energy-efficient computational offloading scheme, IEEE Trans. Cognit. Commun. Netw., 5, 1060, 10.1109/TCCN.2019.2930521 · DOI
  33. Chen, 2019, iRAF: a deep reinforcement learning approach for collaborative mobile edge computing IoT networks, IEEE Internet Things J., 6, 7011, 10.1109/JIOT.2019.2913162 · DOI
  34. Wang, 2019, Resource allocation in information-centric wireless networking with D2D-enabled MEC: a deep reinforcement learning approach, IEEE Access, 7, 114935, 10.1109/ACCESS.2019.2935545 · DOI
  35. Thar, 2019, A deep learning model generation framework for virtualized multi-access edge cache management, IEEE Access, 7, 62734, 10.1109/ACCESS.2019.2916080 · DOI
  36. Pham, 2019, Coalitional games for computation offloading in NOMA-enabled multi-access Edge computing, IEEE Trans. Veh. Technol.
  37. Nassar, 2019, Reinforcement learning for adaptive resource allocation in fog RAN for IoT with heterogeneous latency requirements, IEEE Access, 7, 128014, 10.1109/ACCESS.2019.2939735 · DOI
  38. Lei, 2019, Multiuser resource control with deep reinforcement learning in IoT edge computing, IEEE Internet Things J., 6, 10119, 10.1109/JIOT.2019.2935543 · DOI
  39. Wang, 2020, A game-based computation offloading method in vehicular multi-access edge computing networks, IEEE Internet Things J.

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Metadata

Authors
Tefera, G., She, K., Shelke, M., & Ahmed, A.
ORCID
Tefera, Getenet
Venue
Sustainable Computing: Informatics and Systems
Publisher
Elsevier BV
Published
2021-06
Volume
30
Pages
100555
ISSN
2210-5379
DOI
10.1016/j.suscom.2021.100555
Cited by
9
References
2
Language
en
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