Introduction to algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Analysis of shortest-path routing algorithms in a dynamic network environment
ACM SIGCOMM Computer Communication Review
TCP/IP illustrated (vol. 1): the protocols
TCP/IP illustrated (vol. 1): the protocols
Deriving traffic demands for operational IP networks: methodology and experience
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
On the marginal utility of network topology measurements
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multicast-based inference of network-internal loss characteristics
IEEE Transactions on Information Theory
Application of a network dynamics analysis tool to mobile ad hoc networks
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
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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.