Evaluation of the performance challenges in automatic traffic report generation with huge data volumes

TL;DR

This paper examines the performance challenges in automated traffic report generation for large-scale IT networks, focusing on data processing, KPI selection, and system design for efficiency and cost-effectiveness.

Contribution

It analyzes the impact of data volume on report generation and compares high-level and low-level approaches for system design in commodity hardware.

Findings

Large data volumes require careful KPI selection.

High-level languages offer speed and versatility advantages.

Design for commodity hardware enables cost-effective analysis.

Abstract

In this paper we analyze the performance issues involved in the generation of auto- mated traffic reports for large IT infrastructures. Such reports allows the IT manager to proactively detect possible abnormal situations and roll out the corresponding cor- rective 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 is 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.