  Network Traffic Monitoring and Analysis (NTMA) represents a key component for
network management, especially to guarantee the correct operation of
large-scale networks such as the Internet. As the complexity of Internet
services and the volume of traffic continue to increase, it becomes difficult
to design scalable NTMA applications. Applications such as traffic
classification and policing require real-time and scalable approaches. Anomaly
detection and security mechanisms require to quickly identify and react to
unpredictable events while processing millions of heterogeneous events. At
last, the system has to collect, store, and process massive sets of historical
data for post-mortem analysis. Those are precisely the challenges faced by
general big data approaches: Volume, Velocity, Variety, and Veracity. This
survey brings together NTMA and big data. We catalog previous work on NTMA that
adopt big data approaches to understand to what extent the potential of big
data is being explored in NTMA. This survey mainly focuses on approaches and
technologies to manage the big NTMA data, additionally briefly discussing big
data analytics (e.g., machine learning) for the sake of NTMA. Finally, we
provide guidelines for future work, discussing lessons learned, and research
directions.
