References of "Magaña, Eduardo"
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See detailKISS Methodologies for Network Management and Anomaly Detection
Vega Moreno, Carlos Gonzalo UL; Aracil, Javier; Magaña, Eduardo

in Vega Moreno, Carlos Gonzalo; Aracil, Javier; Magaña, Eduardo (Eds.) KISS Methodologies for Network Management and Anomaly Detection (2018)

Current networks are increasingly growing in size, complexity and the amount of monitoring data that they produce, which requires complex data analysis pipelines to handle data collection, centralization ... [more ▼]

Current networks are increasingly growing in size, complexity and the amount of monitoring data that they produce, which requires complex data analysis pipelines to handle data collection, centralization and analysis tasks. Literature approaches, include the use of custom agents to harvest information and large data centralization systems based on clusters to achieve horizontal scalability, which are expensive and difficult to deploy in real scenarios. In this paper we propose and evaluate a series of methodologies, deployed in real industrial production environments, for network management, from the architecture design to the visualization system as well as for the anomaly detection methodologies, that intend to squeeze the vertical resources and overcome the difficulties of data collection and centralization. [less ▲]

Detailed reference viewed: 82 (0 UL)
Peer Reviewed
See detailOn the design and performance evaluation of automatic traffic report generation systems with huge data volumes
Vega Moreno, Carlos Gonzalo UL; Miravalls Sierra, Eduardo; Julián Moreno, Guillermo et al

in International Journal of Network Management (2018), 28(6), 2044

Summary In this paper, we analyze the performance issues involved in the generation of automated traffic reports for large IT infrastructures. Such reports allow the IT manager to proactively detect ... [more ▼]

Summary In this paper, we analyze the performance issues involved in the generation of automated traffic reports for large IT infrastructures. Such reports allow the IT manager to proactively detect possible abnormal situations and roll out the corresponding corrective actions. With the ever-increasing bandwidth of current networks, the design of automated traffic report generation systems is very challenging. In a first step, the huge volumes of collected traffic are transformed into enriched flow records obtained from diverse collectors and dissectors. Then, such flow records, along with time series obtained from the raw traffic, are further processed to produce a usable report. As will be shown, the data volume in flow records turns out to be very large as well and requires careful selection of the key performance indicators (KPIs) to be included in the report. In this regard, we discuss the use of high-level languages versus low-level approaches, in terms of speed and versatility. Furthermore, our design approach is targeted for rapid development in commodity hardware, which is essential to cost-effectively tackle demanding traffic analysis scenarios. Actually, the paper shows feasibility of delivering a large number of KPIs, as will be detailed later, for several TBytes of traffic per day using a commodity hardware architecture and high-level languages. [less ▲]

Detailed reference viewed: 78 (2 UL)