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 updating (rolling-over) various reports and statistics over highvolume 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.

Large-scale network traffic monitoring with DBStream, a system for rolling big data analysis / Arian, Bar; Finamore, Alessandro; Pedro, Casas; Lukasz, Golab; Mellia, Marco. - STAMPA. - 1:(2014), pp. 165-170. ((Intervento presentato al convegno Big Data (Big Data), 2014 IEEE International Conference on tenutosi a Washington, DC nel October 2014 [10.1109/BigData.2014.7004227].

Large-scale network traffic monitoring with DBStream, a system for rolling big data analysis

FINAMORE, ALESSANDRO;MELLIA, Marco
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 updating (rolling-over) various reports and statistics over highvolume 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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2602579
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