With the fast growth in network-connected vehicular devices, the Internet of Vehicles (IoV) has many advances in terms of size and speed for Intelligent Transportation System (ITS) applications. As a result, the amount of produced data and computational loads has increased intensely. A solution to handle the vast volume of workload has been traditionally cloud computing such that a substantial delay is encountered in the processing of workload, and this has made a serious challenge in the ITS management and workload distribution. Processing a part of workloads at the edge-systems of the vehicular network can reduce the processing delay while striking energy restrictions by migrating the mission of handling workloads from powerful servers of the cloud to the edge systems with limited computing resources at the same time. Therefore, a fair distribution method is required that can evenly distribute the workloads between the powerful data centers and the light computing systems at the edge of the vehicular network. In this paper, a kind of Genetic Algorithm (GA) is exploited to optimize the power consumption of edge systems and reduce delays in the processing of workloads simultaneously. By considering the battery depreciation, the supporting power supply, and the delay, the proposed method can distribute the workloads more evenly between cloud and fog servers so that the processing delay decreases significantly. Also, in comparison with the existing methods, the proposed algorithm performs significantly better in both using green energy for recharging the fog server batteries and reducing the delay in processing data.

中文翻译：

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智能车联网云雾架构中工作负载的优化分配


随着联网车辆设备的快速增长，车联网 (IoV) 在智能交通系统 (ITS) 应用的规模和速度方面取得了许多进步。结果，产生的数据量和计算负荷急剧增加。传统的处理海量工作负载的解决方案是云计算，但工作负载的处理会遇到较大的延迟，这对ITS管理和工作负载分配提出了严峻的挑战。在车载网络的边缘系统处理部分工作负载可以通过将处理工作负载的任务从强大的云服务器迁移到计算资源有限的边缘系统来减少处理延迟，同时消除能源限制。因此，需要一种公平的分配方法，能够在强大的数据中心和车联网边缘的轻计算系统之间均匀分配工作负载。本文利用一种遗传算法（GA）来优化边缘系统的功耗，同时减少工作负载处理的延迟。通过考虑电池折旧、配套电源和延迟，该方法可以在云和雾服务器之间更均匀地分配工作负载，从而使处理延迟显着降低。此外，与现有方法相比，所提出的算法在使用绿色能源为雾服务器电池充电和减少处理数据的延迟方面都表现得更好。