DRL-Based Long-Term Resource Planning for Task Offloading Policies in Multiserver Edge Computing Networks-Reference-Cited by-同舟云学术

DRL-Based Long-Term Resource Planning for Task Offloading Policies in Multiserver Edge Computing Networks

Published:2022-12 Issue:4 Volume:19 Page:4151-4164
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Li Haiyuan¹^ORCID,Assis Karcius Day R.¹^ORCID,Yan Shuangyi¹^ORCID,Simeonidou Dimitra¹^ORCID

Affiliation:

1. High Performance Networks Group, Smart Internet Lab, School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, Faculty of Engineering, University of Bristol, Bristol, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4275028/10034788/09831431.pdf?arnumber=9831431

Reference43 articles.

1. Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds

2. ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments

3. Online Computation Offloading in NOMA-Based Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

4. A Frontier: Dependable, Reliable and Secure Machine Learning for Network/System Management

5. Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

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