Sublessor: A Cost-Saving Internet Transit Mechanism for Cooperative MEC Providers in Industrial Internet of Things

Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a bet...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-09, Vol.19 (9), p.1-12
Main Authors: Chen, Sheng, Zhang, Qihang, Dong, Xiaodong, Tao, Xiaoyi, Li, Keqiu, Qiu, Tie, Lee, Ivan
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Communications traffic
Costs
Deep reinforcement learning (DRL)
Edge computing
End users
Industrial applications
Industrial Internet of Things
industrial internet of things (IIoT)
Integer programming
Internet
Internet of Things
internet transit pricing
Machine learning
Mixed integer
Mobile computing
mobile edge computing (MEC)
Pricing
Quality of service architectures
Reinforcement learning
Wide area networks
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Summary:Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a better quality of service and become MEC providers. These MEC providers require to rent wide area network (WAN) connections to transfer industrial data, which is a considerable expense. In this article, we propose a framework called Sublessor to reduce the WAN transmission cost for a group of cooperative MEC providers. The key idea of Sublessor is allowing some specific MEC providers to act as Internet transit brokers, transmitting not only their own network traffic but also the traffic of their partners under a reasonable reselling price. This article formulates the problem as a mixed integer programming and finds the most suitable broker number and corresponding reselling price without damaging the profit of both brokers and partners by a deep-reinforcement-learning-based algorithm. Experimental results show that our algorithm can significantly reduce the traffic transmission cost by up to 35%.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3230689