A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems

Author:

Hortelano DiegoORCID,de Miguel Ignacio,Barroso Ramón J. Durán,Aguado Juan Carlos,Merayo Noemí,Ruiz Lidia,Asensio Adrian,Masip-Bruin Xavi,Fernández Patricia,Lorenzo Rubén M.,Abril Evaristo J.

Funder

España Ministerio de Ciencia e Innovación

Ministerio de Ciencia e Innovación

Consejería de Educación, Junta de Castilla y León

European Regional Development Fund

Ministerio de Economía y Competitividad

Agencia Estatal de Investigación

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Reference191 articles.

1. Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing;Alameddine;IEEE J. Sel. Areas Commun.,2019

2. Deep reinforcement learning aided task partitioning and computation offloading in mobile edge computing;Ale,2021

3. Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning;Ale;IEEE Trans. Cogn. Commun. Netw.,2021

4. Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA;Alfakih;IEEE Access,2020

5. A user profile-aware policy-based management framework for greening the cloud;Alhaddadin,2014

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