For a synchronised service, average latencies are not a key issue, since the exact synchronisation point can be set to compensate this average, while the jitter and worst case latencies are the key performance metrics: the hard-real-time nature of global coordination means that compensation is not feasible, thus all latency variations will end up in overhead (guard-bands in the time frame), or in forwarding errors. While some works related to high-performance network applications only consider average throughput and do not care about the latencies introduced by the OS, it has been recently noticed that controlling the maximum latencies can be important too. While modern hardware (even off-the-shelf hardware) is generally able to provide the low latencies needed for PF, the software stack is more problematic, as the latencies generated by an Operating System (OS) kernel can be large enough to compromise the correct forwarding of packets or to force to use a low bitrate, as it will be shown in Section 3. The results have proved the appropriateness of time series evolving fuzzy engine for network classification. The performance has been analyzed by carrying out several experiments on real-world traffic dataset and under extreme difficult situation of high-speed networks. The normal situation is a time series data of an ordered sequence of traffic information variable values at equally spaced time intervals. After capturing traffic data, the system analyzes it to establish a model of normal network situation. This paper proposes a novel evolving fuzzy system to discriminate anomalies by inspecting the network traffic. However, there is a need for a comprehensive data processing system to extract valuable insights from network traffic big data and learn the normal and attack network situations. Recent research in big data analytic filed has produced several novel large-scale data processing systems. As the network traffic is becoming big, heterogeneous, and very fast, traffic analysis could be considered as big data analytic task. Monitoring and analyzing network traffic are very crucial in discriminating the malicious attack.
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