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Title: Energy-Aware Resource Management in Vehicular Edge Computing Systems
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 more » of 51% energy savings compared to a baseline that offloads vehicles' workloads only to RSUs. « less
Authors:
; ;
Award ID(s):
1724227
Publication Date:
NSF-PAR ID:
10183074
Journal Name:
Proc. of the IEEE International Conference on Cloud Engineering (IC2E 2020)
Page Range or eLocation-ID:
49 to 58
Sponsoring Org:
National Science Foundation
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