Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

2019/04/01 by Yuxuan Sun, Xueying Guo, Jinhui Song +4 · 12 citations
Computer Science · Decision Sciences · #Age of Information Optimization #Advanced Bandit Algorithms Research #IoT and Edge/Fog Computing

paper · doi:10.1109/tvt.2019.2895593

Abstract

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.

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• A comprehensive review of task offloading methods in Vehicular Fog Computing
• A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management
• Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
• A Management Framework for Vehicular Cloudtoward Economic and Environmental Efficiency
• Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control
• Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks. [europepmc]
• Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing. [europepmc]
• Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems. [europepmc]
• Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing. [europepmc]
• A Game-Based Computing Resource Allocation Scheme of Edge Server in Vehicular Edge Computing Networks Considering Diverse Task Offloading Modes. [europepmc]
• A DAG-Based Offloading Strategy with Dynamic Parallel Factor Adjustment for Edge Computing in IoV. [europepmc]

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