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DOE OSTI · 1831862

XRootD popularity on hadoop clusters

Meoni, Marco·Boccali, Tommaso·Magini, Nicolò·Menichetti, Luca·Giordano, Domenico
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Abstract

Performance data and metadata of the computing operations at the CMS experiment are collected through a distributed monitoring infrastructure, currently relying on a traditional Oracle database system. This paper shows how to harness Big Data architectures in order to improve the throughput and the efficiency of such monitoring. A large set of operational data - user activities, job submissions, resources, file transfers, site efficiencies, software releases, network traffic, machine logs - is being injected into a readily available Hadoop cluster, via several data streamers. The collected metadata is further organized running fast arbitrary queries; this offers the ability to test several Map&Reduce-based frameworks and measure the system speed-up when compared to the original database infrastructure. By leveraging a quality Hadoop data store and enabling an analytics framework on top, it is possible to design a mining platform to predict dataset popularity and discover patterns and correlations.

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97 MATHEMATICS AND COMPUTING

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Meoni, Marco, Boccali, Tommaso, Magini, Nicolò, Menichetti, Luca, Giordano, Domenico. 2017-11-22. XRootD popularity on hadoop clusters. https://doi.org/10.1088/1742-6596%2F898%2F7%2F072027

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