scholarly journals An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1221 ◽  
Author(s):  
Liu ◽  
Chen ◽  
Wu ◽  
Deng ◽  
Liu ◽  
...  
Keyword(s):  
High Speed ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
Prediction Algorithm ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Computing Power

With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots; it can also predict the location of the vehicle and return the result to the vehicle.

scholarly journals Reliable Mobile Edge Service Offloading Based on P2P Distributed Networks

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 821 ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Luyu Tang ◽  
Feiyan Guo ◽  
Jiaming Zhang
Keyword(s):  
Fault Tolerance ◽  
High Speed ◽  
Distributed Networks ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
Computation Offloading ◽  
Task Completion ◽  
Computing Power ◽  
Moving Vehicles ◽  
Geographically Distributed

Intelligent vehicles and their applications increasingly demand high computing power and low task delays, which poses significant challenges for providing reliable and efficient vehicle services. Mobile edge computing (MEC) is a new model that reduces the completion time of tasks and improves vehicle service by performing computation offloading near the moving vehicles. Considering the high-speed mobility of the vehicles and the unstable connection of the wireless cellular network, symmetric and geographically distributed edge servers are regarded as peers in a peer-to-peer (P2P) network, and a P2P-based vehicle edge offloading model is proposed in this paper to determine the optimal offloading server for the vehicle and the offloading ratio of tasks to achieve the goal of minimizing execution time. Because the edge computing infrastructure is deployed at the edge of the network, the data in the edge nodes are easily damaged or lost. Therefore, a P2P-based edge node fault tolerance mechanism is proposed to improve the reliability and fault tolerance of the system. The feasibility and effectiveness of our proposed system have been verified through simulation experiments, which greatly reduces the task completion delay.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment

2020 ◽  
Author(s):  
João Luiz Grave Gross ◽  
Cláudio Fernando Fernando Resin Geyer
Keyword(s):  
Energy Consumption ◽  
Cost Minimization ◽  
Cost Model ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Data Intensive ◽  
Battery Capacity ◽  
Cost Efficient

In a scenario with increasingly mobile devices connected to the Internet, data-intensive applications and energy consumption limited by battery capacity, we propose a cost minimization model for IoT devices in a Mobile Edge Computing (MEC) architecture with the main objective of reducing total energy consumption and total elapsed times from task creation to conclusion. The cost model is implemented using the TEMS (Time and Energy Minimization Scheduler) scheduling algorithm and validated with simulation. The results show that it is possible to reduce the energy consumed in the system by up to 51.61% and the total elapsed time by up to 86.65% in the simulated cases with the parameters and characteristics defined in each experiment.

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 Latency-aware secure transmission in MEC-assisted HSR communication systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Hao Xu ◽  
Ke Li ◽  
Jianfeng Cheng ◽  
Bo Jiang ◽  
Huai Yu
Keyword(s):  
Energy Consumption ◽  
Communication Systems ◽  
High Speed ◽  
Wireless Channel ◽  
Edge Computing ◽  
Mobile Users ◽  
Mobile Edge Computing ◽  
Backhaul Link ◽  
Time Latency ◽  
Secure Transmission

AbstractMobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

scholarly journals Container-Based Fast Service Migration Method for Mobile Edge Computing

2021 ◽  
Author(s):  
Xianyu Meng ◽  
Wei Lu
Keyword(s):  
Dynamic Loading ◽  
File System ◽  
High Reliability ◽  
Rapid Development ◽  
Edge Computing ◽  
Migration Time ◽  
Mobile Edge Computing ◽  
Computing Center ◽  
Service Migration ◽  
Iot Devices

Mobile edge computing (MEC) provides users with low-latency, high-bandwidth, and high-reliability services by migrating the computing power of the cloud computing center to the edge of the network. It is thus being considered an effective solution for the contradiction between the limited computing capabilities of Internet of Things (IoT) devices and the rapid development of delay-sensitive real-time applications. In this study, we propose and design a container union file system based on the differencing hard disk and dynamic loading strategy to address the excessively long migration time caused by the bundling transmission of the file system and container images during container-based service migration. The proposed method involves designing a mechanism based on a remote dynamic loading strategy to avoid the downloading of all container images, thereby reducing the long preparation time before which stateless migration can begin. Furthermore, in view of the excessive latency of the edge service during the stateful migration process, a strategy for avoiding the transmission of the underlying file system and container images is designed to optimize the service interruption time and service quality degradation time. Experiments show that the proposed method and strategy can effectively reduce the migration time of container-based services.

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