Research on Design and Application of Mobile Edge Computing Model Based on SDN

2020 ◽  
Author(s):  
Shaohua Cao ◽  
Zhihao Wang ◽  
Yizhi Chen ◽  
Dingde Jiang ◽  
Yang Yan ◽  
...  
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Model ◽  
Model Based ◽  
Design And Application

scholarly journals A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 102295-102303 ◽  
Author(s):  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Jordi Mongay Batalla
Keyword(s):  
Energy Conservation ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Efficient Computation ◽  
Computing Model

scholarly journals A user interest community evolution model based on subgraph matching for social networking in mobile edge computing environments

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Liang Jiang ◽  
Lu Liu ◽  
Jingjing Yao ◽  
Leilei Shi
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Fundamental Problem ◽  
Rapid Development ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
User Interest ◽  
Subgraph Matching ◽  
Model Based ◽  
Community Evolution

AbstractWith the rapid development of mobile edge computing, mobile social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. Internet of People such as online social networks is the next frontier for the Internet of Things. The combination of social networking and mobile edge computing has an important application value and is the development trend of future networks. However, how to detect evolutionary communities accurately and efficiently in dynamic heterogeneous social networks remains a fundamental problem. In this paper, a novel User Interest Community Evolution (UICE) model based on subgraph matching is proposed for accurately detecting the corresponding communities in the evolution of the user interest community. The community evolutionary events can be quickly captured including forming, dissolving, evolving and so on with the introduction of core subgraph. A variant of subgraph matching, called Subgraph Matching with Dynamic Weight (SMDW), is proposed to solve the problem of updating the core subgraph due to the change of core user’s interest when tracking evolutionary communities. Finally, the experiments based on the real datasets have been designed to evaluate the performance of the proposed model by comparing it with the state-of-art methods in this area and complete data processing through the local edge computing layer. The experimental results demonstrate that the UICE model presented in this paper has achieved better accuracy, higher efficiency and better scalability against existing methods.

scholarly journals The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Min Zhu
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Education System ◽  
Edge Computing ◽  
Performance Ratio ◽  
Mobile Edge Computing ◽  
Network Education ◽  
Model Based ◽  
The Impact ◽  
A Performance

This article first established a university network education system model based on physical failure repair behavior at the big data infrastructure layer and then examined in depth the complex common causes of multiple data failures in the big data environment caused by a single physical machine failure, all based on the principle of mobile edge computing. At the application service layer, a performance model based on queuing theory is first established, with the amount of available resources as a conditional parameter. The model examines important events in mobile edge computing, such as queue overflow and timeout failure. The impact of failure repair behavior on the random change of system dynamic energy consumption is thoroughly investigated, and a system energy consumption model is developed as a result. The network education system in colleges and universities includes a user login module, teaching resource management module, student and teacher management module, online teaching management module, student achievement management module, student homework management module, system data management module, and other business functions. Later, the theory of mobile edge computing proposed a set of comprehensive evaluation indicators that characterize the relevance, such as expected performance and expected energy consumption. Based on these evaluation indicators, a new indicator was proposed to quantify the complex constraint relationship. Finally, a functional use case test was conducted, focusing on testing the query function of online education information; a performance test was conducted in the software operating environment, following the development of the test scenario, and the server’s CPU utilization rate was tested while the software was running. The results show that the designed network education platform is relatively stable and can withstand user access pressure. The performance ratio indicator can effectively assist the cloud computing system in selecting a more appropriate option for the migrated traditional service system.

scholarly journals Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinhui Ding ◽  
Wenjuan Zhang
Keyword(s):  
Game Theory ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Transmission Rate ◽  
Mobile User ◽  
Edge Computing ◽  
Communication Model ◽  
Mobile Edge Computing ◽  
Computing Model ◽  
The Game Theory

Due to the limited computing resources of the mobile edge computing (MEC) server, a massive Internet of things device computing unloading strategy using game theory in mobile edge computing is proposed. First of all, in order to make full use of the massive local Internet of things equipment resources, a new MEC system computing an unloading system model based on device-to-device (D2D) communication is designed and modeled, including communication model, task model, and computing model. Then, by using the utility function, the parameters are substituted into it, and the optimization problem with the goal of maximizing the number of CPU cycles and minimizing the energy consumption is constructed with the unloading strategy and power as constraints. Finally, the game theory is used to solve the problem of computing offload. Based on the proposed beneficial task offload theory, combined with the mobile user device computing offload task amount, transmission rate, idle device performance, and other factors, the computing offload scheme suitable for their own situation is selected. The simulation results show that the proposed scheme has better convergence characteristics, and, compared with other schemes, the proposed scheme significantly improves the amount of data transmission and reduces the energy consumption of the task.

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