Ultra-Low Latency Multi-Task Offloading in Mobile Edge Computing
With the development of computer technology, computational-intensive and delay-sensitive applications are emerging endlessly, and they are limited by the computing power and battery life of Smart Mobile Devices (SMDs). Mobile edge computing (MEC) is a computation model with great potential to meet a...
Main Authors: | Hongxia Zhang, Yongjin Yang, Xingzhe Huang, Chao Fang, Peiying Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9360636/ |
Similar Items
-
Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing
by: Chunyang Hu, et al.
Published: (2021-08-01) -
Research on task offloading based on deep reinforcement learning in mobile edge environment
by: Gao Xia, et al.
Published: (2020-01-01) -
Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning
by: Ming Sun, et al.
Published: (2021-08-01) -
A new task offloading algorithm in edge computing
by: Zhenjiang Zhang, et al.
Published: (2021-01-01) -
Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning
by: Haifeng Lu, et al.
Published: (2020-01-01)