VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems
VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems
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DOI：
10.1109/tmc.2021.3089338
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发表时间：
2023-02
影响因子：
7.9
通讯作者：
Tayebeh Bahreini;Marco Brocanelli;Daniel Grosu
中科院分区：
文献类型：
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作者：
Tayebeh Bahreini;Marco Brocanelli;Daniel Grosu
在车辆边缘计算（VEC）系统中，连接的电动汽车（EV）的计算资源用于满足车辆的低延迟计算要求。然而，繁重工作负载的本地执行可能会消耗EV中的大量能量。提高能源效率的一种有前途的方法是在连接的电动汽车之间共享和协调计算资源。然而，车辆未来位置的不确定性使得很难决定哪些车辆参与资源共享以及它们共享资源多长时间，以便所有参与者都从资源共享中受益。在本文中，我们提出了VECMAN，能源感知资源管理框架VEC系统由两个算法组成：（i）资源选择器算法，确定参与的车辆和资源共享周期的持续时间;和（ii）能源管理器算法，管理计算资源的参与车辆的计算能源消耗最小化的目的。我们评估了所提出的算法，并表明它们大大降低了车辆的计算能耗相比，国家的最先进的基线。具体来说，与本地执行工作负载的基准相比，我们的算法实现了7%到18%的节能，与将车辆工作负载卸载到RSU的基准相比，平均节能13%。
In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles (EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles’ computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7 and 18 percent energy savings compared to a baseline that executes workload locally and an average of 13 percent energy savings compared to a baseline that offloads vehicles’ workloads to RSUs.