att_abstract={{Data center networks have started to play increasingly important roles
in today's Internet. Popular web-based services and critical
enterprise applications are hosted in large data centers. More recent
advances like cloud computing and cellular-based data usage have only
increased the importance of data centers. With the increasing
importance, however, also comes increasing complexity. Supporting the
wide-array of applications and traffic types while meeting all
their performance and security requirements results in complex network
designs. The result of this complexity is that managing these networks
has never been more difficult.

In this paper, we focus on providing one of the key building blocks of
network management: the ability to determine how traffic flows in the
network. This information is fundamental to many different network
management tasks including troubleshooting, capacity
planning, and what-if analysis. Towards that end, we present Chartis, a
system which performs per-packet path inference in a data
center. Chartis takes as input device configurations and the
network's physical topology and outputs the path of a packet in the
network. Specifically, Chartis performs per-hop path inference based on a
simplified model of layer-3 routing, layer-2 switching, and the most
commonly used routing and forwarding mechanisms.
To show Chartis diverse applicability, we perform path inference
within a campus network and on multiple data centers serving the 3G
network of a major cellular service provider. Using routing information
collected from these networks, we validate the correctness of the inferred
paths. Our results show that Chartis can quickly and accurately
determine paths traversed by packets even in complex data center
networks, making it a valuable addition to a network operator's
	att_authors={as1818, vg7777, sl1858, em5708},
	att_copyright_notice={{The definitive version was published in  2012. {{, 2012-10-22}}{{, http://www.cnsm-conf.org/2012/index.html}}
	att_tags={Data center,  path inference,  configuration analysis},
	author={Aman Shaikh and Vijay Gopalakrishnan and Seungjoon Lee and Kyriaki Levanti and Hyong Kim and Emmanuil Mavrogiorgis},
	institution={{CNSM (Conference on Network and Service Management)}},
	title={{Path Inference in Data Center Networks}},