Understanding MEC Empowered Vehicle Task Offloading Performance in 6G Networks
Understanding MEC Empowered Vehicle Task Offloading Performance in 6G Networks
复制标题
了解 6G 网络中 MEC 赋能的车辆任务卸载性能
DOI：
10.1007/s12083-021-01285-1
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Jing Bai
中科院分区：
文献类型：
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作者：
Lili Jiang;Xiaolin Chang;Jelena V. Misic;Vojislav B. Misic;Jing Bai
第六代（6 G）车载网络预计将更大规模、异构、动态和智能化，并且预计将满足来自车载应用的各种服务质量（QoS）和体验质量（QoE）要求。本文旨在定量研究MEC-Cloud编排范式在为车辆提供异构和优先级6 G-V2X（Vehicle-to-Everything）服务方面的能力。我们开发了一个可扩展的分析模型，用于捕获6 G V2X服务过程，其中延迟敏感的车辆任务可以由于车辆移动性而在MEC服务器之间动态迁移。计算性能指标，包括任务拒绝概率和平均任务响应延迟的公式，推导。仿真结果与数值解相结合，证明了模型和度量公式的近似精度。通过数值分析，说明了各种参数对6 G V2X业务性能的影响。
The sixth-generation (6G) vehicular networks are expected to be much more large-scaled, heterogeneous, dynamic and intelligent, and are expected to meet diverse Quality of Service (QoS) and Quality of Experience (QoE) requirements from vehicular applications. This paper aims to quantitatively investigate the capability of the MEC-Cloud orchestration paradigm in provisioning heterogeneous and priority 6G-V2X (Vehicle-to-Everything) service to vehicles. We develop a scalable analytic model for capturing the 6G V2X service process, in which a latency-sensitive vehicle task can dynamically migrate between MEC servers due to vehicle mobility. Formulas for calculating performance metrics, including task rejection probability and mean task response delay, are derived. Simulation results are combined with the numerical solution to demonstrate the approximate accuracy of the model and metric formulas. Numerical analysis is applied to illustrate the impact of various parameters on the 6G V2X service performance.