Resource Allocation in Fog RAN for Heterogeneous IoT Environments Based on Reinforcement Learning
Resource Allocation in Fog RAN for Heterogeneous IoT Environments Based on Reinforcement Learning
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DOI：
10.1109/icc.2019.8761626
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
A. Nassar;Y. Yilmaz
中科院分区：
文献类型：
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作者：
A. Nassar;Y. Yilmaz
雾无线接入网（F-RAN）是近年来为满足物联网（IoT）应用的低延迟通信需求而提出的。我们考虑将雾节点的有限资源顺序分配给具有不同延迟需求的异构物联网应用程序的问题。具体来说，对于每个服务请求，雾节点需要决定是在本地为该用户服务，为其提供低延迟通信服务，还是将其提交给云控制中心，为未来的用户保留有限的雾资源。我们将问题表述为马尔可夫决策过程（MDP），并通过强化学习（RL）提出了最优决策策略。所提出的资源分配方法从物联网环境中学习如何在两个冲突的目标之间取得适当的平衡，最大化总服务效用并最小化雾节点的空闲时间。各种物联网环境的大量仿真结果证实了所提出的基于rl的资源分配方法的理论基础。
Fog radio access network (F-RAN) has been recently proposed to satisfy the low-latency communication requirements of Internet of Things (IoT) applications. We consider the problem of sequentially allocating the limited resources of a fog node to a heterogeneous population of IoT applications with varying latency requirements. Specifically, for each service request, the fog node needs to decide whether to serve that user locally to provide it with low-latency communication service or to refer it to the cloud control center to keep the limited fog resources available for future users. We formulate the problem as a Markov Decision Process (MDP), for which we present the optimal decision policy through Reinforcement Learning (RL). The proposed resource allocation method learns from the IoT environment how to strike the right balance between two conflicting objectives, maximizing the total served utility and minimizing the idle time of the fog node. Extensive simulation results for various IoT environments corroborate the theoretical underpinnings of the proposed RL-based resource allocation method.