Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach

In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be off...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 68; H. 5; S. 5087 - 5099
Hauptverfasser: Zhou, Zhenyu, Feng, Junhao, Chang, Zheng, Shen, Xuemin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Ad hoc networks
Algorithms
Batteries
Computer simulation
consensus ADMM
Convex functions
Data transmission
Edge computing
Energy
Energy conservation
Energy consumption
energy efficiency
Energy transmission
Handover
Optimization
Provisioning
Topology
Vehicle-to-infrastructure
vehicular edge computing
vehicular networks
Workload
workload offloading
Workloads
ISSN:0018-9545, 1939-9359
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Zusammenfassung:In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be offloaded based on the dynamic states of energy consumption and latency in local computing, data transmission, workload execution and handover, is still an open issue. In this paper, we study the energy-efficient workload offloading problem and propose a low-complexity distributed solution based on consensus alternating direction method of multipliers. By incorporating a set of local variables for each UE, the original problem, in which the optimization variables of UEs are coupled together, is transformed into an equivalent general consensus problem with separable objectives and constraints. The consensus problem can be further decomposed into a bunch of subproblems, which are distributed across UEs and solved in parallel simultaneously. Finally, the proposed solution is validated based on a realistic road topology of Beijing, China. Simulation results have demonstrated that significant energy saving gain can be achieved by the proposed algorithm.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2905432