Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
A distributed approach to measure IP traffic matrices
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Robust traffic matrix estimation with imperfect information: making use of multiple data sources
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
A first look at modern enterprise traffic
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
OpenFlow: enabling innovation in campus networks
ACM SIGCOMM Computer Communication Review
NOX: towards an operating system for networks
ACM SIGCOMM Computer Communication Review
Implementing an OpenFlow switch on the NetFPGA platform
Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
Leveraging router programmability for traffic matrix computation
Proceedings of the Workshop on Programmable Routers for Extensible Services of Tomorrow
OFRewind: enabling record and replay troubleshooting for networks
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
OFLOPS: an open framework for openflow switch evaluation
PAM'12 Proceedings of the 13th international conference on Passive and Active Measurement
FlowSense: monitoring network utilization with zero measurement cost
PAM'13 Proceedings of the 14th international conference on Passive and Active Measurement
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In this paper we present OpenTM, a traffic matrix estimation system for OpenFlow networks. OpenTM uses built-in features provided in OpenFlow switches to directly and accurately measure the traffic matrix with a low overhead. Additionally, OpenTM uses the routing information learned from the OpenFlow controller to intelligently choose the switches from which to obtain flow statistics, thus reducing the load on switching elements. We explore several algorithms for choosing which switches to query, and demonstrate that there is a trade-off between accuracy of measurements, and the worst case maximum load on individual switches, i.e., the perfect load balancing scheme sometimes results in the worst estimate, and the best estimation can lead to worst case load distribution among switches. We show that a non-uniform distribution querying strategy that tends to query switches closer to the destination with a higher probability has a better performance compared to the uniform schemes. Our test-bed experiments show that for a stationary traffic matrix OpenTM normally converges within ten queries which is considerably faster than existing traffic matrix estimation techniques for traditional IP networks.