Abstract-- Human error in configuring routers undermines attempts to provide reliable, predictable end-to-end performance on IP networks. Manual configuration, while expensive and errorprone, is the dominant mode of operation, especially for large enterprise networks. These networks often lack the basic building blocks--an accurate equipment inventory, a debugged initial configuration, and a specification of local configuration policies--to support the holy grail of automation. We argue that migrating an existing network to automated configuration is a rich and challenging research problem rooted in data analysis and in the modeling of network protocols and operational practices. We propose a novel, bottom-up approach that proceeds in three phases: (i) analysis of configuration data to summarize the existing network state and uncover configuration problems; (ii) data mining to identify the network's local configuration policies and violations of these policies; and ultimately (iii...
Donald F. Caldwell, Anna Gilbert, Joel Gottlieb, A