Toward scaling network emulation using topology partitioning

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Abstract
Scalability is the primary challenge to studying large complex network systems with network emulation. This paper studies topology partitioning, assigning disjoint pieces of the network topology across processors, as a technique to increase emulation capacity with increasing hardware resources. We develop methods to create partitions based on expected communication across the topology. Our evaluation methodology quantifies the communication overhead or efficiency of the resulting partitions. We implement and contrast three partitioning strategies in ModelNet, a large-scale network emulator, using different topologies and uniform communication patterns. Results show that standard graph partitioning algorithms can double the efficiency of the emulation for Internet-like topologies relative to random partitioning.

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