GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds-Reference-Cited by-同舟云学术

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GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds

Published:2024-08 Issue:4 Volume:21 Page:4226-4242
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Liu Zhang¹^ORCID,Huang Lianfen¹^ORCID,Gao Zhibin²^ORCID,Luo Manman³,Hosseinalipour Seyyedali⁴^ORCID,Dai Huaiyu⁵^ORCID

Affiliation:

1. Department of Informatics and Communication Engineering, Xiamen University, Xiamen, China

2. Navigation Institute, Jimei University, Xiamen, China

3. Department of Electronic Engineering, Xiamen University, Xiamen, China

4. Department of Electrical Engineering, University at Buffalo SUNY, Buffalo, NY, USA

5. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province of China

Xiamen Major science and Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4275028/10643497/10497174.pdf?arnumber=10497174

Reference53 articles.

1. Furion: Engineering High-Quality Immersive Virtual Reality on Today's Mobile Devices

2. Coping With Emerging Mobile Social Media Applications Through Dynamic Service Function Chaining

3. DAG Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm

4. A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures

5. List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table

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