A Novel Deep Reinforcement Learning Based Dependent Tasks Offloading in Multi-Access Edge Computing

Jie Lin; Junqi Cai; Hanlin Zhang; Ruining Zhang; Xinyu Yang; Peng Zhao

doi:10.2139/ssrn.4196751

ScienceGate Book Chapters

JOURNAL ARTICLE

A Novel Deep Reinforcement Learning Based Dependent Tasks Offloading in Multi-Access Edge Computing

Jie Lin Junqi Cai Hanlin Zhang Ruining Zhang Xinyu Yang Peng Zhao

Year: 2022 Journal: SSRN Electronic Journal Publisher: RELX Group (Netherlands)

DOI: 10.2139/ssrn.4196751

Get Full-Text PDF Get Analytical Report

Keywords:

Reinforcement learning Computer science Edge computing Enhanced Data Rates for GSM Evolution Edge device Artificial intelligence Distributed computing Human–computer interaction Computer network Cloud computing Operating system

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Blockchain Technology Applications and Security

Physical Sciences → Computer Science → Information Systems

Age of Information Optimization

Physical Sciences → Computer Science → Computer Networks and Communications

A Novel Deep Reinforcement Learning Based Dependent Tasks Offloading in Multi-Access Edge Computing

Metrics

Citation History

Topics

Related Documents

Offloading dependent tasks in multi-access edge computing: A multi-objective reinforcement learning approach

Deep Reinforcement Learning for Dependent Task Offloading in Multi-Access Edge Computing

A Deep Reinforcement Learning Approach for Dependent Task Offloading in Multi-Access Edge Computing

IMOPSOQ: Offloading Dependent Tasks in Multi-access Edge Computing

Dependent tasks joint scheduling and offloading for edge computing based on deep reinforcement learning