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34_4 - Multiple Source Multiple Destination Network...

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Multiple Source, Multiple Destination Network Tomography Michael Rabbat Rice University Houston, Texas Email: [email protected] Robert Nowak Rice University Houston, Texas Email: [email protected] Mark Coates McGill University Montreal, QC Email: [email protected] Abstract — The problem of identifying topology and inferring link-level performance parameters such as packet drop rate or delay variance using only end-to-end measurements is commonly referred to as network tomography. This paper describes a collaborative framework for performing network tomography on topologies with multiple sources and multiple destinations, with- out assuming the topology to be known. Using multiple sources potentially provides a more accurate and refined characterization of the internal network. We present a novel multiple source active measurement procedure using a semi-randomized probing scheme and packet arrival order measurements which do not require precise synchronization between the participating hosts. A decision-theoretic framework is developed enabling the joint characterization of topology and internal performance. We design a statistical test based on the Generalized Likelihood Ratio Test and Wilks’ Theorem. The test quantifies the tradeoff between network topology complexity and performance estimation, and identifies when measurements made by the two sources can be combined to achieve reduced variance performance estimates. The performance and efficacy of the algorithm are assessed through ns-2 simulations and experiments over the Internet. Method Keywords — Statistics, Network measurements, Simu- lations, Experimentation with real networks/testbeds. I. N ETWORK T OMOGRAPHY Assessing and predicting internal network behavior is of fundamental importance in a variety of problems such as routing optimization, network management, and anomaly de- tection. However, acquiring direct internal measurements from all parts of the network is not practical due to the distributed nature of the Internet. Additionally, one cannot rely on internal network elements to respond with special purpose messages (i.e. ICMP timestamp exceeded) due to growing security con- cerns. Those who do have access to internal measurements are nearly always restricted from sharing the data for proprietary and privacy reasons. For the purpose of network management, direct link-level measurements are critical for the low-level analysis of equip- ment conditions. However, traditional fault alarms are only triggered after failures occur, and passive measurements gen- erate large amounts of data which are not easily processed online. Ciavattone et al. describe a practical system using ac- tive end-to-end measurements to augment traditional network operations measurements allowing them to proactively detect impairments and react quickly to performance degradation [1].
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