research-article

GAPRO: An Adaptive User-centric Resource Allocation and Task Offloading Strategy for Multi-access Edge Computing

Authors:

Tran Quang Anh,

Binh Minh Nguyen,

Huynh Thi Thanh BinhAuthors Info & Claims

SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology

Pages 260 - 267

https://doi.org/10.1145/3628797.3628953

Published: 07 December 2023 Publication History

Abstract

Multi-access Edge Computing (MEC) is a promising technology to enhance the performance of latency-critical applications by providing additional computing capabilities at network edges. However, in traditional cellular-based MEC, users at the cell edge experience signal attenuation and inter-cell interference, resulting in increased risks of transmission outages and offloading failures. We introduce an integration of MEC functionalities and the User-centric Network, harnessing the strengths of both paradigms. The main objective is to minimize overall delay and energy consumption while adhering to specific delay and energy constraints. To address this problem, we formulate it as a Markov Decision Process and then propose a Generalized Advantage Policy for Resource allocation and Offloading strategy to jointly optimize task partitioning, transmit power control, and computing resource allocation. Simulation results demonstrate that the proposed optimization scheme significantly reduces energy consumption and delays for users, while also guaranteeing delay and battery constraints.

References

[1]

Hussein A Ammar, Raviraj Adve, Shahram Shahbazpanahi, Gary Boudreau, and Kothapalli Venkata Srinivas. 2021. User-centric cell-free massive MIMO networks: A survey of opportunities, challenges and solutions. IEEE Communications Surveys & Tutorials 24, 1 (2021), 611–652.

[2]

Ta Huu Binh, Do Bao Son, Hiep Vo, Binh Minh Nguyen, and Huynh Thi Thanh Binh. 2023. Reinforcement Learning for Optimizing Delay-Sensitive Task Offloading in Vehicular Edge-Cloud Computing. IEEE Internet of Things Journal (2023).

[3]

Emil Björnson and Luca Sanguinetti. 2019. Making cell-free massive MIMO competitive with MMSE processing and centralized implementation. IEEE Transactions on Wireless Communications 19, 1 (2019), 77–90.

[4]

Chen Chen, Lanlan Chen, Lei Liu, Shunfan He, Xiaoming Yuan, Dapeng Lan, and Zhuang Chen. 2020. Delay-Optimized V2V-Based Computation Offloading in Urban Vehicular Edge Computing and Networks. IEEE Access 8 (2020), 18863–18873.

[5]

Lixing Chen, Cong Shen, Pan Zhou, and Jie Xu. 2021. Collaborative Service Placement for Edge Computing in Dense Small Cell Networks. IEEE Transactions on Mobile Computing 20, 2 (2021), 377–390.

Digital Library

[6]

Zheng Chen, Emil Björnson, and Erik G Larsson. 2019. Dynamic resource allocation in co-located and cell-free massive MIMO. IEEE Transactions on Green Communications and Networking 4, 1 (2019), 209–220.

[7]

Fabio Giust, Xavier Costa-Perez, and Alex Reznik. 2017. Multi-access edge computing: An overview of ETSI MEC ISG. IEEE 5G Tech Focus 1, 4 (2017), 4.

[8]

Shuyang Li, Xiaohui Hu, and Yongwen Du. 2021. Deep reinforcement learning and game theory for computation offloading in dynamic edge computing markets. IEEE Access 9 (2021), 121456–121466.

[9]

Jun Liu, Pan Li, Jianqi Liu, and Jinfeng Lai. 2019. Joint Offloading and Transmission Power Control for Mobile Edge Computing. IEEE Access 7 (2019), 81640–81651.

[10]

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning. PMLR, 1928–1937.

[11]

Sudarshan Mukherjee and Jemin Lee. 2020. Edge computing-enabled cell-free massive MIMO systems. IEEE Transactions on Wireless Communications 19, 4 (2020), 2884–2899.

[12]

Elina Nayebi, Alexei Ashikhmin, Thomas L Marzetta, and Bhaskar D Rao. 2016. Performance of cell-free massive MIMO systems with MMSE and LSFD receivers. In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 203–207.

[13]

Hien Quoc Ngo, Alexei Ashikhmin, Hong Yang, Erik G Larsson, and Thomas L Marzetta. 2017. Cell-free massive MIMO versus small cells. IEEE Transactions on Wireless Communications 16, 3 (2017), 1834–1850.

Digital Library

[14]

Phu X. Nguyen, Dinh-Hieu Tran, Oluwakayode Onireti, Phu Tran Tin, Sang Quang Nguyen, Symeon Chatzinotas, and H. Vincent Poor. 2021. Backscatter-Assisted Data Offloading in OFDMA-Based Wireless-Powered Mobile Edge Computing for IoT Networks. IEEE Internet of Things Journal 8, 11 (2021), 9233–9243.

[15]

Cunhua Pan, Maged Elkashlan, Jiangzhou Wang, Jinhong Yuan, and Lajos Hanzo. 2018. User-centric C-RAN architecture for ultra-dense 5G networks: Challenges and methodologies. IEEE Communications Magazine 56, 6 (2018), 14–20.

[16]

Langtian Qin, Hancheng Lu, and Feng Wu. 2022. When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization. IEEE Communications Magazine 61, 1 (2022), 114–120.

[17]

John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015).

[18]

Shuming Seng, Changqing Luo, Xi Li, Heli Zhang, and Hong Ji. 2020. User matching on blockchain for computation offloading in ultra-dense wireless networks. IEEE Transactions on Network Science and Engineering 8, 2 (2020), 1167–1177.

[19]

Do Bao Son, Vu Tri An, Trinh Thu Hai, Binh Minh Nguyen, Nguyen Phi Le, and Huynh Thi Thanh Binh. 2021. Fuzzy Deep Q-learning Task Offloading in Delay Constrained Vehicular Fog Computing. In 2021 International Joint Conference on Neural Networks (IJCNN). 1–8.

[20]

Do Bao Son, Ta Huu Binh, Hiep Khac Vo, Binh Minh Nguyen, Huynh Thi Thanh Binh, and Shui Yu. 2022. Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing. Engineering Applications of Artificial Intelligence 113 (2022), 104898. https://doi.org/10.1016/j.engappai.2022.104898

Digital Library

[21]

Do Bao Son, Hiep Khac Vo, Ta Huu Binh, Tran Hoang Hai, Binh Minh Nguyen, and Huynh Thi Thanh Binh. 2023. Reinforcement-Learning-Based Deadline Constrained Task Offloading Schema for Energy Saving in Vehicular Edge Computing System. In 2023 International Joint Conference on Neural Networks (IJCNN). 01–08.

[22]

Huynh Thi Thanh Binh, Nguyen Phi Le, Nguyen Binh Minh, Trinh Thu Hai, Ngo Quang Minh, and Do Bao Son. 2020. A Reinforcement Learning Algorithm for Resource Provisioning in Mobile Edge Computing Network. In 2020 International Joint Conference on Neural Networks (IJCNN). 1–7.

[23]

Kun Wang, Xiaofeng Wang, Xuan Liu, and Alireza Jolfaei. 2020. Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles. IEEE Access 8 (2020), 173779–173789.

[24]

Kai Xiong, Supeng Leng, Xiaosha Chen, Chongwen Huang, Chau Yuen, and Yong Liang Guan. 2020. Communication and Computing Resource Optimization for Connected Autonomous Driving. IEEE Transactions on Vehicular Technology 69, 11 (2020), 12652–12663.

[25]

Xin Yao and Yong Liu. 1997. Fast evolution strategies. In International conference on evolutionary programming. Springer, 149–161.

[26]

Wenhan Zhan, Chunbo Luo, Jin Wang, Chao Wang, Geyong Min, Hancong Duan, and Qingxin Zhu. 2020. Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing. IEEE Internet of Things Journal 7, 6 (2020), 5449–5465.

Cited By

Pilli NMohapatra DReddy S(2025)Review on Meta-heuristic Algorithm-Based Priority-Aware Computation Offloading in Edge Computing SystemJournal of The Institution of Engineers (India): Series B10.1007/s40031-025-01200-9Online publication date: 31-Jan-2025
https://doi.org/10.1007/s40031-025-01200-9

Index Terms

GAPRO: An Adaptive User-centric Resource Allocation and Task Offloading Strategy for Multi-access Edge Computing

Index terms have been assigned to the content through auto-classification.

Recommendations

Optimizing task offloading and resource allocation in edge-cloud networks: a DRL approach
Abstract
Edge-cloud computing is an emerging approach in which tasks are offloaded from mobile devices to edge or cloud servers. However, Task offloading may result in increased energy consumption and delays, and the decision to offload the task is ...
A collaborative optimization strategy for computing offloading and resource allocation based on multi-agent deep reinforcement learning
Abstract
With the emergence of mobile edge computing (MEC), the edge cloud with certain computing power is deployed closer to the mobile device, which can well solve the computing and delay requirements of the mobile device. In 5G ultra-dense ...
Graphical abstract

Display Omitted
Highlights
- A joint optimization strategy of task offloading and resource allocation for 5G ultra-dense networks is proposed.
Joint computation offloading and resource allocation for NOMA-based multi-access mobile edge computing systems
Abstract
Due to explosive growth of computation-intensive applications, multi-access mobile edge computing (MEC) has been proposed as an efficient technology to relieve the burden on mobile users by offloading computation tasks to MEC servers. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology

December 2023

1058 pages

ISBN:9798400708916

DOI:10.1145/3628797

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

SOICT 2023

SOICT 2023: The 12th International Symposium on Information and Communication Technology

December 7 - 8, 2023

Ho Chi Minh, Vietnam

Acceptance Rates

Overall Acceptance Rate 147 of 318 submissions, 46%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
62
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)6

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pilli NMohapatra DReddy S(2025)Review on Meta-heuristic Algorithm-Based Priority-Aware Computation Offloading in Edge Computing SystemJournal of The Institution of Engineers (India): Series B10.1007/s40031-025-01200-9Online publication date: 31-Jan-2025
https://doi.org/10.1007/s40031-025-01200-9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten