Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
By Yuxuan Sun, Xueying Guo, Jinhui Song, Sheng Zhou, Zhiyuan Jiang, Xin Liu, Zhisheng Niu. IEEE Transactions on Vehicular Technology, 2019
325 citations recorded by the source corpus.
The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to $30\%$ compared with the existing upper confidence bound based learning algorithm.
Subfields: Computer Networks and Communications, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/cc7e5c94d33c852ff1201f39101be490adca5510
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In Computer Networks and Communications
In Management Science and Operations Research
Citations in the corpus
- Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “The rapidly changing network structure, communication status, and computing load have led to the dynamics and uncertainty of task offloading [150], making efficient task offloading and resource allocation decisions more difficult.”