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Jinge Huang

dblp:269/7213 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Network optimization and economics · 70% Edge and fog computing · 30%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Edge and fog computing
mobile edge computing
0.712023
Efficient Revenue-Based MEC Server Deployment and Management in Mobile Edge-Cloud Computing · IEEE/ACM Trans. Netw. 2023
Network optimization and economics
resource allocation
0.712023
Efficient Revenue-Based MEC Server Deployment and Management in Mobile Edge-Cloud Computing · IEEE/ACM Trans. Netw. 2023
Network optimization and economics
revenue maximization
0.712023
Efficient Revenue-Based MEC Server Deployment and Management in Mobile Edge-Cloud Computing · IEEE/ACM Trans. Netw. 2023
Network optimization and economics › network design
server placement
0.712023
Efficient Revenue-Based MEC Server Deployment and Management in Mobile Edge-Cloud Computing · IEEE/ACM Trans. Netw. 2023

Methods — techniques the papers use, named apart from their topics

three-layer optimization · 0.7convex optimization · 0.7
YearPublicationVenuePosition
2023 Efficient Revenue-Based MEC Server Deployment and Management in Mobile Edge-Cloud Computing
abstract
With the explosive growth of mobile applications, the development of mobile edge computing (MEC) has been greatly promoted since it can ably improve the quality of service for mobile applications by providing low latency and high-quality computation services. Most existing works focus on improving the efficiency of MEC with an assumption that the MEC servers have already been deployed. However, without appropriate deployment of MEC servers, the profitability of the MEC system can be significantly restrained, which hinders the rapid promotion of the MEC. To address this issue, we formulate an MEC server deployment problem for the MEC operator as a revenue maximization problem. Firstly, we model and analyze the various factors that affect the revenue. Secondly, we formulate a revenue maximization problem, which is NP-hard, but it is proved to be convex with respect to the total available computation units. Based on this feature, we propose a three-layer optimization algorithm, named EDM, in which the location, the deployed computation units, and the wholesaled computation resources are determined gradually, to maximize the total revenue. Experimental results demonstrate that the proposed EDM algorithm has significant advantages on revenue improvement compared to competitive benchmarks.
Yongmin Zhang, Wei Wang 0343, Ju Ren 0001, Jinge Huang, Shibo He, Yaoxue Zhang
IEEE/ACM Trans. Netw.4