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Task Replication for Vehicular Edge Computing: A Combinatorial Multi-Armed Bandit based Approach. (arXiv:1807.05718v1 [cs.IT])
来源于:arXiv
In vehicular edge computing (VEC) system, some vehicles with surplus
computing resources can provide computation task offloading opportunities for
other vehicles or pedestrians. However, vehicular network is highly dynamic,
with fast varying channel states and computation loads. These dynamics are
difficult to model or to predict, but they have major impact on the quality of
service (QoS) of task offloading, including delay performance and service
reliability. Meanwhile, the computing resources in VEC are often redundant due
to the high density of vehicles. To improve the QoS of VEC and exploit the
abundant computing resources on vehicles, we propose a learning-based task
replication algorithm (LTRA) based on combinatorial multi-armed bandit (CMAB)
theory, in order to minimize the average offloading delay. LTRA enables
multiple vehicles to process the replicas of the same task simultaneously, and
vehicles that require computing services can learn the delay performance of
other vehicles 查看全文>>