基于自适应q学习的车辆雾资源分配模型

Md Tahmid Hossain, R. E. Grande
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摘要

车载云计算(VCC)在车载应用需求和间歇性网络条件下显示出许多缺点。车载雾计算是一种支持和促进城市服务和资源有效共享的新方法。关于车辆资源管理的各种工作都试图使用各种方法来处理非常动态的车辆环境,例如基于策略的贪婪和随机技术。然而,车辆的高机动性带来了许多问题,影响了服务的一致性、效率和质量。结合强化学习的自适应车辆雾可以处理机动性,并在所有雾中正确分配服务和资源。因此,我们引入了一种自适应资源管理模型,使用cloudlet驻留时间进行资源估计,使用数学公式进行Fog选择,使用强化学习进行迭代审查和反馈机制生成最佳资源分配策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Q-leaming-supported Resource Allocation Model in Vehicular Fogs
Vehicular Cloud Computing (VCC) exhibits many drawbacks with the demands of vehicular applications and intermittent network conditions. Vehicular Fog computing is a novel method for supporting and promoting the effective sharing of services and resources in urban areas. Diverse works on vehicular resource management have sought to handle the very dynamic vehicular environment using various methods, such as policy-based greedy and stochastic techniques. Nevertheless, high vehicular mobility poses many issues that compromise service consistency, efficiency, and quality. Adaptive vehicular Fogs incorporating Reinforcement Learning can deal with mobility and correctly distribute services and resources across all Fogs. Thus, we introduce an adaptive resource management model using cloudlet dwell time for resource estimation, mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy.
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