论文标题
一个模拟物联网现实流数据数据的框架
A Framework for Simulating Real-world Stream Data of the Internet of Things
论文作者
论文摘要
随着物联网（IoT）设备的数量的迅速增长，在现实世界中，流数据的数量和类型正在迅速增加。不幸的是，流数据具有现实世界中无限和周期性波动的特征，这会导致效率低下的流处理任务问题。在这项研究中，我们报告了我们在此问题上的最新工作，重点是模拟流数据。首先，我们探讨了物联网实际流数据的特征，这有助于我们了解现实世界中的流数据。其次，提出了模拟流数据的管道，该管道可以准确有效地模拟流数据的特征，以提高特定任务的效率。最后，我们设计并实施了一个新颖的框架，该框架可以模拟各种流数据以用于相关的流处理任务。为了验证所提出的框架的有效性，我们将此框架应用于流处理系统中运行的流处理任务。实验结果表明，使用我们提出的仿真框架至少24次加速了相关的流处理任务，以确保波动性和流数据的趋势的前提。
With the rapid growth in the number of devices of the Internet of Things (IoT), the volume and types of stream data are rapidly increasing in the real world. Unfortunately, the stream data has the characteristics of infinite and periodic volatility in the real world, which cause problems with the inefficient stream processing tasks. In this study, we report our recent efforts on this issue, with a focus on simulating stream data. Firstly, we explore the characteristics of the real-world stream data of the IoT, which helps us to understand the stream data in the real world. Secondly, the pipeline of simulating stream data is proposed, which can accurately and efficiently simulate the characteristics of the stream data to improve efficiency for specific tasks. Finally, we design and implement a novel framework that can simulate various stream data for related stream processing tasks. To verify the validity of the proposed framework, we apply this framework to stream processing task running in the stream processing system. The experimental results reveal that the related stream processing task is accelerated by at least 24 times using our proposed simulation framework with the premise of ensuring volatility and trends of stream data.
