Learning-Based Intent-Aware Task Offloading for Air-Ground Integrated Vehicular Edge Computing

doi:10.1109/TITS.2020.3027437

Learning-Based Intent-Aware Task Offloading for Air-Ground Integrated Vehicular Edge Computing

2021-08-01

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DOI：10.1109/TITS.2020.3027437

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Existing task offloading mechanisms are developed on some single and rigid quality of service (QoS) performance metrics, which is widely apart from satisfying the true intent of a user vehicle (UV), thereby resulting in low quality of experience (QoE), large queuing latency, and poor reliability. There is an unprecedented demand for an intent-aware task offloading strategy that provides improved QoE and guarantees reliability. In this paper, we develop a novel task offloading framework for air-ground integrated vehicular edge computing (AGI-VEC), which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. IUCB can achieve three-dimension intent awareness including QoE awareness, URLLC awareness, and trajectory similarity awareness. Simulation results demonstrate that IUCB significantly outperforms existing EMM, sleeping-UCB, and UCB mechanisms in terms of QoE, end-to-end delay, queuing delay, throughput, and times of task offloading failure.

Keywords:

Task analysis

Servers

Quality of experience

Ultra reliable low latency communication

Delays

Optimization

Air-ground integrated vehicular edge computing

task offloading

intent awareness

ultra-reliable low latency communications (URLLC)

quality of experience (QoE)

trajectory similarity

Learning-Based Intent-Aware Task Offloading for Air-Ground Integrated Vehicular Edge Computing

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