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You're in PublicationsLarge-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis

 

Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis

Arian Bar, Alessandro Finamore, Pedro Casas, Luckasz Golab, Marco Mellia

Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis

IEEE International Conference on Big Data 2014 - IEEE BigData 2014, Washington DC,, 27 October 2014

 

Abstract

The complexity of the Internet has rapidly increased, making it more important and challenging to design scalable network monitoring tools. Network monitoring typically requires rolling data analysis, i.e., continuously and incrementally up- dating (rolling-over) various reports and statistics over high- volume data streams. In this paper, we describe DBStream, which is an SQL-based system that explicitly supports incremental queries for rolling data analysis. We also present a performance comparison of DBStream with a parallel data processing engine (Spark), showing that, in some scenarios, a single DBStream node can outperform a cluster of ten Spark nodes on rolling network monitoring workloads. Although our performance evaluation is based on network monitoring data, our results can be generalized to other big data problems with high volume and velocity. 

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