DRAM: A DRL-based resource allocation scheme for MAR in MEC
Mobile Edge Computing (MEC) and 5G technology allow clients to access computing resources at the network frontier, which paves the way for applying Mobile Augmented Reality (MAR) applications. Under the MEC paradigm, MAR clients can offload complex tasks to the MEC server and enhance the human perce...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822000633 |
| _version_ | 1827917690174439424 |
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| author | Tongyu Song Xuebin Tan Jing Ren Wenyu Hu Sheng Wang Shizhong Xu Xiong Wang Gang Sun Hongfang Yu |
| author_facet | Tongyu Song Xuebin Tan Jing Ren Wenyu Hu Sheng Wang Shizhong Xu Xiong Wang Gang Sun Hongfang Yu |
| author_sort | Tongyu Song |
| collection | DOAJ |
| description | Mobile Edge Computing (MEC) and 5G technology allow clients to access computing resources at the network frontier, which paves the way for applying Mobile Augmented Reality (MAR) applications. Under the MEC paradigm, MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real environment. However, the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network frontier. In this paper, we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into consideration. We first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning paradigm. Then, we propose DRAM, a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in MEC. We also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation problems. The simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’ adaptation algorithms. |
| first_indexed | 2024-03-13T03:32:01Z |
| format | Article |
| id | doaj.art-0aff2eb8d31a4931bdc08210e1a5a615 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-03-13T03:32:01Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-0aff2eb8d31a4931bdc08210e1a5a6152023-06-24T05:18:00ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-06-0193723733DRAM: A DRL-based resource allocation scheme for MAR in MECTongyu Song0Xuebin Tan1Jing Ren2Wenyu Hu3Sheng Wang4Shizhong Xu5Xiong Wang6Gang Sun7Hongfang Yu8School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaCorresponding author.; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaCorresponding author.; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR ChinaMobile Edge Computing (MEC) and 5G technology allow clients to access computing resources at the network frontier, which paves the way for applying Mobile Augmented Reality (MAR) applications. Under the MEC paradigm, MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real environment. However, the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network frontier. In this paper, we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into consideration. We first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning paradigm. Then, we propose DRAM, a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in MEC. We also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation problems. The simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’ adaptation algorithms.http://www.sciencedirect.com/science/article/pii/S2352864822000633Mobile edge computingMobile augmented realityDeep reinforcement learningResource allocation |
| spellingShingle | Tongyu Song Xuebin Tan Jing Ren Wenyu Hu Sheng Wang Shizhong Xu Xiong Wang Gang Sun Hongfang Yu DRAM: A DRL-based resource allocation scheme for MAR in MEC Digital Communications and Networks Mobile edge computing Mobile augmented reality Deep reinforcement learning Resource allocation |
| title | DRAM: A DRL-based resource allocation scheme for MAR in MEC |
| title_full | DRAM: A DRL-based resource allocation scheme for MAR in MEC |
| title_fullStr | DRAM: A DRL-based resource allocation scheme for MAR in MEC |
| title_full_unstemmed | DRAM: A DRL-based resource allocation scheme for MAR in MEC |
| title_short | DRAM: A DRL-based resource allocation scheme for MAR in MEC |
| title_sort | dram a drl based resource allocation scheme for mar in mec |
| topic | Mobile edge computing Mobile augmented reality Deep reinforcement learning Resource allocation |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822000633 |
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