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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Potential Game Based Distributed IoV Service Offloading With Graph Attention Networks in Mobile Edge Computing
AU - Jiang, Qinting
AU - Xu, Xiaolong
AU - Bilal, Muhammad
AU - Crowcroft, Jon
AU - Liu, Qi
AU - Dou, Wanchun
AU - Jiang, Jingyan
PY - 2024/3/12
Y1 - 2024/3/12
N2 - Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance.
AB - Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance.
KW - Computer Science Applications
KW - Mechanical Engineering
KW - Automotive Engineering
U2 - 10.1109/tits.2024.3369190
DO - 10.1109/tits.2024.3369190
M3 - Journal article
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
SN - 1524-9050
ER -