Energy-Aware Resource Management in Vehicular Edge Computing Systems
Energy-Aware Resource Management in Vehicular Edge Computing Systems
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DOI：
10.1109/ic2e48712.2020.00012
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发表时间：
2020-04
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影响因子：
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通讯作者：
Tayebeh Bahreini;Marco Brocanelli;Daniel Grosu
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文献类型：
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作者：
Tayebeh Bahreini;Marco Brocanelli;Daniel Grosu
互联电动汽车的低延迟要求及其不断增长的计算需求导致必须将计算节点从云数据中心移动到边缘节点，如路边单元（RSU）。然而，将所有车辆的工作负载卸载到RSU可能无法很好地扩展到越来越多的车辆和工作负载。为了解决这个问题，可以将计算节点直接安装在智能车辆上，使每辆车都可以在本地执行繁重的工作量，从而形成车载边缘计算系统。另一方面，这些计算节点可能会消耗电动车辆中的大量能量。因此，重要的是要管理连接的电动汽车的资源，以最大限度地减少其能源消耗。在本文中，我们提出了一种算法，管理连接的电动汽车的计算节点，以最大限度地减少能源消耗。该算法通过利用各种性能水平的计算能力的离散设置来实现联网电动汽车的节能。我们评估了所提出的算法，并表明它大大降低了车辆的计算能耗相比，国家的最先进的基线。具体而言，与本地执行工作负载的基线相比，我们的算法实现了15-85%的节能，与仅将车辆的工作负载卸载到RSU的基线相比，平均节能51%。
The low-latency requirements of connected electric vehicles and their increasing computing needs have led to the necessity to move computational nodes from the cloud data centers to edge nodes such as road-side units (RSU). However, offloading the workload of all the vehicles to RSUs may not scale well to an increasing number of vehicles and workloads. To solve this problem, computing nodes can be installed directly on the smart vehicles, so that each vehicle can execute the heavy workload locally, thus forming a vehicular edge computing system. On the other hand, these computational nodes may drain a considerable amount of energy in electric vehicles. It is therefore important to manage the resources of connected electric vehicles to minimize their energy consumption.In this paper, we propose an algorithm that manages the computing nodes of connected electric vehicles for minimized energy consumption. The algorithm achieves energy savings for connected electric vehicles by exploiting the discrete settings of computational power for various performance levels. We evaluate the proposed algorithm and show that it considerably reduces the vehicles’ computational energy consumption compared to state-of-the-art baselines. Specifically, our algorithm achieves 15-85% energy savings compared to a baseline that executes workload locally and an average of 51% energy savings compared to a baseline that offloads vehicles’ workloads only to RSUs.