Cooperative mobile edge computing-cloud computing in Internet of vehicle: Architecture and energy-efficient workload allocation,Transactions on Emerging Telecommunications Technologies

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Cooperative mobile edge computing-cloud computing in Internet of vehicle: Architecture and energy-efficient workload allocation
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-08-05 , DOI: 10.1002/ett.4095
Xiaohui Gu ₁ , Guoan Zhang ₁ , Yujie Cao ₁

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With the increasing number of vehicles, the generating vehicular data exceeds the capacity of mobile edge computing (MEC). Therefore, studying the interaction and collaboration of edge computing and cloud computing is of significance to provide vehicular users with low-latency high-rate services. This paper first proposes a MEC-cloud computing collaboration architecture for Internet of vehicles, then designs the interconnection/interaction framework between MEC and cloud computing. We consider reducing computation delay and power consumption, and formulate an energy-efficient workload allocation problem with load balancing and dynamic voltage frequency scaling technology, to obtain the optimal workload allocations of MEC and cloud computing. We then present the overall distribution optimization algorithm to solve this problem. The simulation and numerical results show that by saving communication bandwidth and reducing transmission delay, MEC significantly enhances the performance of cloud computing. Besides, the proposed workload balance scheme is better than the benchmark schemes in terms of power consumption and latency.

中文翻译：

车联网协同移动边缘计算-云计算：架构与节能工作负载分配

随着车辆数量的增加，产生的车辆数据超过了移动边缘计算（MEC）的能力。因此，研究边缘计算与云计算的交互与协同，对于为车载用户提供低延迟高速率的服务具有重要意义。本文首先提出了一种MEC-云计算车联网协同架构，然后设计了MEC与云计算之间的互联/交互框架。我们考虑减少计算延迟和功耗，并通过负载均衡和动态电压频率缩放技术制定节能的工作负载分配问题，以获得 MEC 和云计算的最佳工作负载分配。然后我们提出整体分布优化算法来解决这个问题。仿真和数值结果表明，MEC通过节省通信带宽和减少传输延迟，显着提升了云计算的性能。此外，所提出的工作负载平衡方案在功耗和延迟方面优于基准方案。

更新日期：2020-08-05

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