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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.

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Abstract-The ever increase of advanced services offered by modern cellular networks require stringent Quality of Service (QoS) guarantee, obtained as the typical result of many optimization procedures. In this paper the phenomenon of dropped calls, one of the most important indices of QoS in a large scale well-established cellular network, has been analyzed. We verified from measured data traffic that, in a well-established cellular network, models available in literature are useless to pursue the objective of service optimization: many phenomena, neglected till now, heavily influence the call termination. To relate the drop call probability to these phenomena, an original analytical model has been developed. The obtained results, validated by experimental measures taken from a real network, can allow the network operator to optimize system performance improving the offered Quality of Service and their revenue.

In this contribution we address the problem of using cellular network signaling for inferring real-time road traffic information. We survey and categorize the approaches that have been proposed in the literature for a cellular-based road monitoring system and identify advantages and limitations. We outline a unified framework that encompasses UMTS and GPRS data collection in addition to GSM, and prospectively combines passive and active monitoring techniques. We identify the main research challenges that must be faced in designing and implementing such an intelligent road traffic estimation system via third-generation cellular networks.