Analysis and simulation of a fair queueing algorithm
SIGCOMM '89 Symposium proceedings on Communications architectures & protocols
Policing congestion response in an internetwork using re-feedback
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Improved Forwarding Architecture and Resource Management for Multi-Core Software Routers
NPC '09 Proceedings of the 2009 Sixth IFIP International Conference on Network and Parallel Computing
Proceedings of the ACM SIGCOMM 2010 conference
Peeling away timing error in netflow data
PAM'11 Proceedings of the 12th international conference on Passive and active measurement
FACT: flow-based approach for connectivity tracking
PAM'11 Proceedings of the 12th international conference on Passive and active measurement
Accurate network anomaly classification with generalized entropy metrics
Computer Networks: The International Journal of Computer and Telecommunications Networking
Is it still possible to extend TCP?
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Multi-resource fair queueing for packet processing
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
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Flow concurrency is the measure of the number of active flows at a given point in time at a given point in the net- work, and can be used as a complement to traffic volume to understand the dynamics of a measured network. It is of particular interest given the spread of devices through the network which keep per-flow state. In this work, we first present a simple methodology for measuring flow concurrency using network flow data, then apply this methodology to a long-term data archive captured at the border of a national-scale research network to measure flow concurrency in selected example network configurations (e.g., at a content consumer network, on a content provider network, at an interconnect point). Flow concurrency is interesting in the context of capacity sharing efforts in two ways. First and most obviously, devices which verify and enforce policy compliance for capacity sharing must keep per-flow state on either end of a flow; flow state requirements therefore dictate where such devices may be placed in the network, and the trust properties of the algorithms they run. Second, as the deployment of flow-state-keeping devices in the network increases, flow state itself becomes a "congestible" resource just as queue space is: future work in capacity sharing may consider addressing this.