URLLC Resource Slicing and Scheduling in 5G Vehicular Edge Computing
2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)(2021)
Abstract
The 5th generation (5G) mobile network technology is accelerating the development of autonomous vehicles by significantly shortening the communication latency and improving the reliability of network connection and transmission. However, as the number of vehicles increases, neither cloud servers nor multi-access edge computing (MEC) servers alone could sufficiently meet the Quality-of-Service (QoS) requirements for computing-intensive vehicle tasks. In this paper, we consider a hierarchical offloading scenario, where vehicle tasks are allowed to execute in MEC servers, convergence servers or cloud servers. To reduce the cost of latency and energy, we optimize the communication and computation resource allocation problem. The optimization problem is converted to a Markov decision process, and deep reinforcement learning is used to tackle the resource slicing and scheduling problem. Simulation results show that the proposed scheme is more resilient and efficient than that of single cloud server offloading or single MEC server offloading.
MoreTranslated text
Key words
Vehicular edge computing,5G,resource slicing,deep reinforcement learning
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2021
被引用8 | 浏览
2018
被引用402 | 浏览
2019
被引用381 | 浏览
2019
被引用914 | 浏览
2020
被引用256 | 浏览
2020
被引用84 | 浏览
2020
被引用275 | 浏览
2020
被引用50 | 浏览
2020
被引用69 | 浏览
2020
被引用13 | 浏览
2021
被引用125 | 浏览
2021
被引用10 | 浏览
2019
被引用66 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest