Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network

By Min Chen, Yixue Hao. 2018

852 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jiajia Liu
2. Sabita Maharjan
3. Xiaofei Wang
4. Yiwen Han
5. Zhenjian Yu
6. Min Chen
7. Yanmin Gong
8. Shimin Gong

In Electrical and Electronic Engineering

1. Sabita Maharjan
2. Yan Zhang
3. Jiajia Liu
4. Zhenjian Yu
5. Min Chen
6. Yanmin Gong
7. Shimin Gong
8. Xu Chen

Citations in the corpus

• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, background): “Several pioneer works have been proposed and realize quite good results in their assuming settings based on convex optimization, game theory and so on [3] [4] .”
• Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems (2020, neutral): “In [8], Chen et al.”
• Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things (2019, methodology): “In [4], without using convex optimization [2] and game theory [3] to solve resource allocation problems in offloading, authors adopted Deep Reinforcement Learning (DRL) to deal with the comprehensive resource allocation in computation offloading for maximizing the long-term benefits of the energy…”
• Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution (2020, background): “In recent years, there has been some research work on the combination of SDN and MEC [4], [15], [16].”
• Resource Scheduling in Edge Computing: A Survey (2021, neutral): “tion simultaneously [51], [107], [112], [131], [250].”
• Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning (2020, methodology): “The task offloading problem in MEC has attracted significant research interests [5], [10], [11], [13], [29], [30], [31], [32], Fig.”
• A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “MEC was applied to ultra dense networks (UDNs) [224], where the authors investi-”
• All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, neutral): “Control and Monitoring [9, 54, 106, 127, 129, 130, 134, 135, 137, 151, 160, 238, 239, 245, 259, 279–287]”
• Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “Chen and Hao [13] investigated the MEC task offloading problem in the software-defined ultradense network aiming at minimizing the total task duration under energy budget constraints.”
• Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond (2019, background): “The authors in [8] proposed task offloading to minimize task duration in software defined ultra dense networks.”