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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System
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Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System

机译：动态物联网系统中计算服务的智能快速自适应卸载算法

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摘要

As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.
机译：由于有限的资源严重限制了大型物联网（IoT）设备的计算性能，因此迫切需要更好的处理能力。作为一项创新技术，多访问边缘计算可以通过将计算密集型服务从设备转移到附近的边缘服务器来提供cloudlet功能。本文提出了一种动态物联网系统的智能快速自适应卸载（IRAO）算法，以提高整体计算性能并同时保持多个参与者的公平性，从而可以实现敏捷的集中控制并解决与卸载策略和资源分配有关的联合优化问题。 。为了减少算法执行时间，我们应用了机器学习方法，并构建了一个基于学习的自适应框架，该框架包括卸载决策，无线电资源切片和算法参数更新。特别是，可以从基于深度神经网络的估计算法快速得出卸载策略，该算法使用经验重播训练方法来提高模型准确性，并采用异步采样技巧来增强训练收敛性能。进行了具有不同参数的广泛仿真，以保持IRAO算法的准确性和效率之间的权衡。与其他候选者相比，结果表明IRAO算法在可伸缩性，有效性和效率方面可以实现卓越的性能。

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