Abstract
To cope with real-time data analysis as the amount of data being exchanged over the network increases, an idea is to re-design algorithms originally implemented on the monitoring probe to work in a distributed manner over a stream-processing platform. In this paper we show preliminary performance analysis of a Twitter trending algorithm when running over BlockMon, an open-source monitoring platform which we extended to run distributed data-analytics algorithms: we show that it performs up to 23.5x and 34.2x faster on BlockMon than on Storm and Apache S4 respectively, two emerging stream-processing platforms.
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Simoncelli, D., Dusi, M., Gringoli, F., Niccolini, S. (2013). Scaling Out the Performance of Service Monitoring Applications with BlockMon. In: Roughan, M., Chang, R. (eds) Passive and Active Measurement. PAM 2013. Lecture Notes in Computer Science, vol 7799. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36516-4_26
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DOI: https://doi.org/10.1007/978-3-642-36516-4_26
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