Abstract: Systems and methods are disclosed for analyzing network traffic data to generate complex statistics associated with the network traffic in real-time through parallel processing and data pipelining. In one implementation, a system includes a processor and a memory. The memory stores instructions that cause the processor to generate a plurality of program instances, wherein a first program instance observes the network traffic and uses a plurality of execution threads to distribute portions of the network traffic to additional program instances tasked with generating statistics associated with the network traffic. In other embodiments, a plurality of additional execution threads to the first program instance are tasked with generating the statistics. In either case, the generated statistics are placed into a data pipeline organized into time intervals of generated statistics, wherein the computation of higher-order statistics are computed as the lower-order time intervals are processed.

Abstract: Systems and methods are disclosed for analyzing network traffic data to generate complex statistics associated with the network traffic in real-time through parallel processing and data pipelining. In one implementation, a system includes a processor and a memory. The memory stores instructions that cause the processor to generate a plurality of program instances, wherein a first program instance observes the network traffic and uses a plurality of execution threads to distribute portions of the network traffic to additional program instances tasked with generating statistics associated with the network traffic. In other embodiments, a plurality of additional execution threads to the first program instance are tasked with generating the statistics. In either case, the generated statistics are placed into a data pipeline organized into time intervals of generated statistics, wherein the computation of higher-order statistics are computed as the lower-order time intervals are processed.