Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach

The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile devices with limited resources face more severe capacity constraints than ever before. As a new conce...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing Vol. 9; no. 3; pp. 1529 - 1541
Main Authors: Wang, Jiadai, Zhao, Lei, Liu, Jiajia, Kato, Nei
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applications programs
Cloud computing
Computer architecture
Deep learning
deep reinforcement learning
Delays
Edge computing
Electronic devices
Machine learning
Mobile computing
Mobile edge computing
Mobile handsets
Resource allocation
Resource management
Routing
Servers
ISSN:2168-6750, 2168-6750
Online Access:Get full text
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Summary:The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile devices with limited resources face more severe capacity constraints than ever before. As a new concept of network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) seems to be a promising solution to meet this emerging challenge. However, MEC also has some limitations, such as the high cost of infrastructure deployment and maintenance, as well as the severe pressure that the complex and mutative edge computing environment brings to MEC servers. At this point, how to allocate computing resources and network resources rationally to satisfy the requirements of mobile devices under the changeable MEC conditions has become a great aporia. To combat this issue, we propose a smart, Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network resources adaptively, reduce the average service time and balance the use of resources under varying MEC environment. Experimental results show that the proposed DRLRA performs better than the traditional OSPF algorithm in the mutative MEC conditions.
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ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2019.2902661