Dynamic Offloading Scheduling Scheme for MEC-enabled Vehicular Networks

The incorporation of mobile edge computing (MEC) into vehicular networks is an inspiring approach to meet the explosive vehicular services demands. The mobile vehicles could execute the computing tasks locally or offload to the nearby MEC servers. However, the mobility of the vehicles brings great c...

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Bibliographic Details
Published in2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops) pp. 206 - 210
Main Authors Wang, Hansong, Li, Xi, Ji, Hong, Zhang, Heli
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
dynamic offloading
Dynamic scheduling
mobile edge computing
Nickel
Processor scheduling
Servers
Task analysis
task partitioning
Uplink
Vehicle dynamics
vehicle mobility
Vehicular networks
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Summary:The incorporation of mobile edge computing (MEC) into vehicular networks is an inspiring approach to meet the explosive vehicular services demands. The mobile vehicles could execute the computing tasks locally or offload to the nearby MEC servers. However, the mobility of the vehicles brings great challenges to finish the offloading before the vehicles moving out of the coverage of original connected MEC servers. To avoid interruption in the offloading process, we investigate the problem and propose a dynamic offloading scheduling scheme for MEC-enabled vehicular networks. The limited resources and variable vehicle speeds are taken into consideration. The whole offloading task is partitioned into small task units (TUs). Then in every cell with respective MEC servers, the optimal offloading ratio and assigned TUs are derived according to the practical constraints like moving speed, cell coverage and transmit data rate. The proposed scheduling scheme is adaptive to the changes of the vehicles' speeds and the wireless transmission environment. Simulation results show that the proposed scheme has a good performance in total offloading time and energy efficiency.
ISSN:2474-9133
DOI:10.1109/ICCChinaW.2018.8674508