Modeling external network behavior by using internal measurements

Network behavior is the set of observations or measurements that can be made about a network over time. The growth of network-based computing and the Internet have ensured that networks can no longer be considered in isolation, as events external to a particular network increasingly impact its behavior. Network management requires that information be known about these events, a task that is not always possible. We present a modeling strategy that takes partial information about a network and uses it to predict the behavior in unmonitored areas. This implementation is based on a meta-heuristic (genetic algorithm), and uses IP-packet information as well as a limited understanding of the external topology. This is then used to model the full topology, routing tables and traffic for the entire network at periodic intervals. The system was tested using the ns-2 network simulator and a Java implementation on a series of cases. The results showed a reasonable level of accuracy in predicting traffic and topology. Performance increased under system load, and at no point did the system generate any additional network traffic. This provides an efficient and effective strategy for network management.

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