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Reload: Deep Reinforcement Learning-based Workload Distribution for Collaborative Edges

delete2025-11-05
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PRE
AI
Y
Yu Liang
葛季栋 (Jidong Ge)
X
Xiangyu Wu
S
Sheng Zhang
S
Shi Wu Wen
B
Bin Luo
DOI:10.1016/j.jpdc.2025.105191delete
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Abstract

Abstract

En 中文
• We consider edge servers executing tasks offloaded from end users collaboratively. • Reload, an intelligent DRL-based task scheduler is designed to schedule tasks. • Reload starts out knowing nothing and gradually learns to make better decisions.

Journal

Journal of Parallel and Distributed Computing cover
Journal of Parallel and Distributed Computing
IF:
4
Papers:
3.8K
Citations:
4.8K

Organization

T
Temple University
Scholars:
1.1W
Papers: 8.8K
Citations: 1.9W
N
nanjing university
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Papers: 5.6W
Citations: 87
N
Nanjing Normal University
Scholars:
1.7W
Papers: 1.3W
Citations: 1.9W
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