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DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing
Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740101/ https://www.ncbi.nlm.nih.gov/pubmed/36501914 http://dx.doi.org/10.3390/s22239212 |
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author | Lim, Ducsun Lee, Wooyeob Kim, Won-Tae Joe, Inwhee |
author_facet | Lim, Ducsun Lee, Wooyeob Kim, Won-Tae Joe, Inwhee |
author_sort | Lim, Ducsun |
collection | PubMed |
description | Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be useless when a process is significantly delayed or a deadline has expired. Due to the uncertain task processing via offloading, it is challenging for each SD to determine its offloading decision (whether to local or remote and drop). This study proposes a deep-reinforcement-learning-based offloading scheduler (DRL-OS) that considers the energy balance in selecting the method for performing a task, such as local computing, offloading, or dropping. The proposed DRL-OS is based on the double dueling deep Q-network (D3QN) and selects an appropriate action by learning the task size, deadline, queue, and residual battery charge. The average battery level, drop rate, and average latency of the DRL-OS were measured in simulations to analyze the scheduler performance. The DRL-OS exhibits a lower average battery level (up to 54%) and lower drop rate (up to 42.5%) than existing schemes. The scheduler also achieves a lower average latency of 0.01 to >0.25 s, despite subtle case-wise differences in the average latency. |
format | Online Article Text |
id | pubmed-9740101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97401012022-12-11 DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing Lim, Ducsun Lee, Wooyeob Kim, Won-Tae Joe, Inwhee Sensors (Basel) Article Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be useless when a process is significantly delayed or a deadline has expired. Due to the uncertain task processing via offloading, it is challenging for each SD to determine its offloading decision (whether to local or remote and drop). This study proposes a deep-reinforcement-learning-based offloading scheduler (DRL-OS) that considers the energy balance in selecting the method for performing a task, such as local computing, offloading, or dropping. The proposed DRL-OS is based on the double dueling deep Q-network (D3QN) and selects an appropriate action by learning the task size, deadline, queue, and residual battery charge. The average battery level, drop rate, and average latency of the DRL-OS were measured in simulations to analyze the scheduler performance. The DRL-OS exhibits a lower average battery level (up to 54%) and lower drop rate (up to 42.5%) than existing schemes. The scheduler also achieves a lower average latency of 0.01 to >0.25 s, despite subtle case-wise differences in the average latency. MDPI 2022-11-26 /pmc/articles/PMC9740101/ /pubmed/36501914 http://dx.doi.org/10.3390/s22239212 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lim, Ducsun Lee, Wooyeob Kim, Won-Tae Joe, Inwhee DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing |
title | DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing |
title_full | DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing |
title_fullStr | DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing |
title_full_unstemmed | DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing |
title_short | DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing |
title_sort | drl-os: a deep reinforcement learning-based offloading scheduler in mobile edge computing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740101/ https://www.ncbi.nlm.nih.gov/pubmed/36501914 http://dx.doi.org/10.3390/s22239212 |
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