Local breakout of mobile access network traffic by mobile edge computing

2016 ◽  
Author(s):  
Seung-Que Lee ◽  
Jin-up Kim
Keyword(s):  
Network Traffic ◽  
Access Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Mobile Access ◽  
Local Breakout

scholarly journals Zone-Based Cooperative Content Caching and Delivery for Radio Access Network With Mobile Edge Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 4031-4044 ◽  
Author(s):  
Ning Wang ◽  
Gangxiang Shen ◽  
Sanjay Kumar Bose ◽  
Weidong Shao
Keyword(s):  
Access Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Radio Access Network ◽  
Content Caching ◽  
Radio Access

scholarly journals Multitier Service Migration Framework Based on Mobility Prediction in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Run Yang ◽  
Hui He ◽  
Weizhe Zhang
Keyword(s):  
Network Traffic ◽  
Scheduling Algorithm ◽  
Prediction Method ◽  
Edge Computing ◽  
Mobility Prediction ◽  
Mobile Edge Computing ◽  
Real World Data ◽  
Service Migration ◽  
Online Prediction ◽  
User Movement

Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real-world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service.

scholarly journals Communication and computation cooperation in cloud radio access network with mobile edge computing

2018 ◽  
Vol 2 (1) ◽  
pp. 43-56
Author(s):  
Tong Li ◽  
Kezhi Wang ◽  
Ke Xu ◽  
Kun Yang ◽  
Chathura Sarathchandra Magurawalage ◽  
...  
Keyword(s):  
Access Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Radio Access Network ◽  
Cloud Radio Access Network ◽  
Radio Access

scholarly journals Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 83 ◽  
Author(s):  
Zhi Li ◽  
Qi Zhu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Completion Time ◽  
Access Network ◽  
Optimization Method ◽  
Edge Computing ◽  
Smart Devices ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Task Completion Time

Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times.

scholarly journals An Intelligent Real-Time Traffic Control Based on Mobile Edge Computing for Individual Private Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sa Math ◽  
Lejun Zhang ◽  
Seokhoon Kim ◽  
Intae Ryoo
Keyword(s):  
Real Time ◽  
Optical Networks ◽  
Traffic Control ◽  
Access Network ◽  
Computer Software ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Great Opportunity ◽  
Real Time Traffic ◽  
Radio Access

The existence of Mobile Edge Computing (MEC) provides a novel and great opportunity to enhance user quality of service (QoS) by enabling local communication. The 5th generation (5G) communication is consisting of massive connectivity at the Radio Access Network (RAN), where the tremendous user traffic will be generated and sent to fronthaul and backhaul gateways, respectively. Since fronthaul and backhaul gateways are commonly installed by using optical networks, the bottleneck network will occur when the incoming traffic exceeds the capacity of the gateways. To meet the requirement of real-time communication in terms of ultralow latency (ULL), these aforementioned issues have to be solved. In this paper, we proposed an intelligent real-time traffic control based on MEC to handle user traffic at both gateways. The method sliced the user traffic into four communication classes, including conversation, streaming, interactive, and background communication. And MEC server has been integrated into the gateway for caching the sliced traffic. Subsequently, the MEC server can handle each user traffic slice based on its QoS requirements. The evaluation results showed that the proposed scheme enhances the QoS and can outperform on the conventional approach in terms of delays, jitters, and throughputs. Based on the simulated results, the proposed scheme is suitable for improving time-sensitive communication including IoT sensor’s data. The simulation results are validated through computer software simulation.

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