A Heuristic Offloading Method for Deep Learning Edge Services in 5G Networks
With the continuous development of the Internet of Things (IoT) and communications technology, especially under the epoch of 5G, mobile tasks with big scales of data have a strong demand in deep learning such as virtual speech recognition and video classification. Considering the limited computing r...
Main Authors: | Xiaolong Xu, Daoming Li, Zhonghui Dai, Shancang Li, Xuening Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8721102/ |
Similar Items
-
A multi-stage heuristic method for service caching and task offloading to improve the cooperation between edge and cloud computing
by: Xiaoqian Chen, et al.
Published: (2022-06-01) -
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
by: Ke Zhang, et al.
Published: (2016-01-01) -
A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing
by: Bo Wang, et al.
Published: (2020-01-01) -
Analysis of Vector Code Offloading Framework in Heterogeneous Cloud and Edge Architectures
by: Junaid Shuja, et al.
Published: (2017-01-01) -
Edge Offloading in Smart Grid
by: Gabriel Ioan Arcas, et al.
Published: (2024-02-01)