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Comparison of MongoDB and Cassandra Databases for supporting Open-Source Platforms tailored to Spectrum Monitoring as-a-Service
IEEE Transactions on Network and Service Management ( IF 4.7 ) Pub Date : 2020-03-01 , DOI: 10.1109/tnsm.2019.2942475
Giuseppe Baruffa , Mauro Femminella , Matteo Pergolesi , Gianluca Reali

Due to the growing number of devices accessing the Internet through wireless networks, the radio spectrum has become a highly contended resource. The availability of low cost radio spectrum monitoring sensors enables a geographically distributed, real-time observation of the spectrum to spot inefficiencies and to develop new strategies for its utilization. The potentially large number of sensors to be deployed and the intrinsic nature of data make this task a Big Data problem. In this work we design, implement, and validate a hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture. This system offers Spectrum Sensing as a Service to let end users easily access and process radio spectrum data. To minimize the latency of services offered by the platform, we fine tune the data processing chain. From the analysis of sensor data characteristics, we design the data models for MongoDB and Cassandra, two popular NoSQL databases. A MapReduce job for spectrum visualization has been developed to show the potential of our approach and to identify the challenges in processing spectrum sensor data. We experimentally evaluate and compare the performance of the two databases in terms of application processing time for different types of queries applied on data streams with heterogeneous generation rate. Our experiments show that Cassandra outperforms MongoDB in most cases, with some exceptions depending on data stream rate.

中文翻译:

比较 MongoDB 和 Cassandra 数据库以支持为频谱监控即服务量身定制的开源平台

由于越来越多的设备通过无线网络访问互联网,无线电频谱已成为一种竞争激烈的资源。低成本无线电频谱监测传感器的可用性使得能够对频谱进行地理分布的实时观察,以发现效率低下的问题并制定新的利用策略。要部署的潜在大量传感器和数据的内在性质使这项任务成为一个大数据问题。在这项工作中,我们设计、实施和验证了受 Lambda 架构启发的用于宽带无线电频谱监测的硬件和软件架构。该系统提供频谱感知即服务,让最终用户轻松访问和处理无线电频谱数据。为了最大限度地减少平台提供的服务的延迟,我们对数据处理链进行了微调。从传感器数据特征分析,我们设计了MongoDB和Cassandra这两个流行的NoSQL数据库的数据模型。已经开发了用于频谱可视化的 MapReduce 作业,以展示我们方法的潜力并确定处理频谱传感器数据的挑战。我们通过实验评估和比较两个数据库在应用程序处理时间方面的性能,这些查询应用于具有异构生成率的数据流上的不同类型的查询。我们的实验表明 Cassandra 在大多数情况下优于 MongoDB,但有一些例外情况取决于数据流速率。已经开发了用于频谱可视化的 MapReduce 作业,以展示我们方法的潜力并确定处理频谱传感器数据的挑战。我们通过实验评估和比较两个数据库在应用程序处理时间方面的性能,这些查询应用于具有异构生成率的数据流上的不同类型的查询。我们的实验表明 Cassandra 在大多数情况下优于 MongoDB,但有一些例外情况取决于数据流速率。已经开发了用于频谱可视化的 MapReduce 作业,以展示我们方法的潜力并确定处理频谱传感器数据的挑战。我们通过实验评估和比较两个数据库在应用程序处理时间方面的性能,这些查询应用于具有异构生成率的数据流上的不同类型的查询。我们的实验表明 Cassandra 在大多数情况下优于 MongoDB,但有一些例外情况取决于数据流速率。
更新日期:2020-03-01
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