Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud
Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud
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DOI：
10.1109/jproc.2019.2901578
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发表时间：
2019-04-01
影响因子：
20.6
通讯作者：
Heegaard, Poul E.
中科院分区：
文献类型：
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作者：
Metzger, Florian;Hossfeld, Tobias;Heegaard, Poul E.
随着物联网（IoT）继续在电信网络中获得吸引力，预计将在不久的将来连接和使用大量设备。为了适当地计划和尺寸网络以及后端云系统以及由此产生的信号负载，采用了流量模型。这些模型旨在以简洁的方式准确捕获和预测物联网流量的属性。为了实现这一目标，过去经常使用基于Palm-Khintchine定理的泊松过程近似。由于建模系统的规模（以及各种IoT网络中的量表差异），因此这种近似值的忠诚度至关重要，因为在实践中，准确测量或模拟大规模的物联网部署非常具有挑战性。本文的主要目标是了解泊松近似模型的准确性水平。为此，我们首先调查了共同的物联网网络属性和网络量表以及流量类型。其次，我们解释并讨论了棕榈 - khintiche定理，如何将其应用于问题，以及使用时可能发生哪些不准确性。基于此，我们得出了有关何时可以假定托管过程的定期IOT流量的准则。最后，我们在IoT Cloud Scaleer用例中评估了我们的方法。
As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm-Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as, in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm-Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.