With the fast development of smart cities and 5G, the amount of mobile data is growing exponentially. The centralized cloud computing mode is hard to support the continuous exchanging and processing of information generated by millions of the Internet-of-Things (IoT) devices. Therefore, mobile-edge computing (MEC) and software-defined networking (SDN) are introduced to form a cloud-edge-terminal collaboration network (CETCN) architecture to jointly utilize the communicational and computational resources. Although the CETCN brings many benefits, there still exist some challenges, such as the unclear operation mode, low utilization of edge resources, as well as the limited energy of terminals. To address these problems, a reinforcement learning-based joint communicational-and-computational resource allocation mechanism (RJCC) is proposed to optimize overall processing delay under energy limits. In RJCC, a $Q$ -learning-based online offloading algorithm and a Lagrange -based migration algorithm are designed to jointly optimize computation offloading across multisegments and on edge platform, respectively. The simulation results show that the proposed RJCC outperforms the delay-optimal, energy-optimal, and edge-to-terminal offloading algorithm by 42%–74% in long-term average energy consumption while maintaining relatively low delay.

中文翻译：

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RJCC：基于强化学习的智慧城市物联网联合通信与计算资源分配机制

随着智慧城市和5G的快速发展，移动数据量呈指数增长。集中式云计算模式很难支持由数百万个物联网（IoT）设备生成的信息的连续交换和处理。因此，引入了移动边缘计算（MEC）和软件定义网络（SDN）以形成云边缘终端协作网络（CETCN）架构，以共同利用通信和计算资源。尽管CETCN带来了很多好处，但是仍然存在一些挑战，例如操作模式不明确，边缘资源利用率低以及终端的能量有限。为了解决这些问题，提出了一种基于强化学习的联合通信与计算资源分配机制（RJCC），以优化能量限制下的整体处理延迟。在RJCC中， $ Q $ 学习的在线分流算法及 拉格朗日 基于迁移的算法旨在分别优化跨多段和边缘平台上的计算分流。仿真结果表明，在保持相对较低的延迟的同时，所建议的RJCC在长期平均能耗方面比延迟优化，能量优化和边缘到终端卸载算法的性能高42％–74％。