Name
Title | ||
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On the design and performance evaluation of automatic traffic report generation systems with huge data volumes |
Abstract | ||
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AbstractSummaryIn 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.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. Our approach is targeted for rapid development in commodity hardware, which is essential to cost‐effectively tackle demanding traffic analysis scenarios. View Figure |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1002/nem.2044 | Periodicals |
DocType | Volume | Issue |
Journal | 28 | 6 |
ISSN | Citations | PageRank |
1099-1190 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Vega | 1 | 7 | 2.88 |
| Eduardo Miravalls-Sierra | 2 | 0 | 0.68 |
| Guillermo Julián-Moreno | 3 | 0 | 0.68 |
| Jorge E. López de Vergara | 4 | 187 | 26.98 |
| Eduardo Magaña | 5 | 44 | 16.34 |
| Javier Aracil | 6 | 213 | 42.23 |