TY - GEN

T1 - Queuing Network Topology Inference Using Passive Measurements

AU - Lin, Yilei

AU - He, Ting

AU - Pang, Guodong

N1 - Funding Information:
This research was partly supported by the National Science Foundation under awards CNS-1946022 and CCF-1813219. ISBN 978-3-903176-39-3©c 2021 IFIP
Publisher Copyright:
© 2021 IFIP.

PY - 2021/6/21

Y1 - 2021/6/21

N2 - In this work, we revisit a classic problem of inferring a tree topology from end-to-end measurements originated by a single source, with two critical differences: (i) instead of relying on measurements with specific correlation across paths that often require active probing, we do not rely on any correlation and can thus utilize passive measurements; (ii) instead of inferring a logical topology that ignores certain nodes, we want to recover the physical topology. Our key idea is to utilize the detailed queuing dynamics inside the network to estimate the number of queues and a certain parameter (residual capacity) of each queue on each measurement path, and then use the estimated parameters as fingerprints to detect shared queues and infer the topology. To this end, we develop a Laplace-transform-based estimator to extract the parameters of a tandem of queues from end-to-end delays, and efficient algorithms to identify the parameters associated with the same queue and infer the topology accordingly. The inferred topology is guaranteed to converge to the ground truth, up to a permutation of queues traversed by the same paths, as the number of measurements increases. Our evaluations validate the proposed solutions against benchmarks and identify potential directions for further improvements.

AB - In this work, we revisit a classic problem of inferring a tree topology from end-to-end measurements originated by a single source, with two critical differences: (i) instead of relying on measurements with specific correlation across paths that often require active probing, we do not rely on any correlation and can thus utilize passive measurements; (ii) instead of inferring a logical topology that ignores certain nodes, we want to recover the physical topology. Our key idea is to utilize the detailed queuing dynamics inside the network to estimate the number of queues and a certain parameter (residual capacity) of each queue on each measurement path, and then use the estimated parameters as fingerprints to detect shared queues and infer the topology. To this end, we develop a Laplace-transform-based estimator to extract the parameters of a tandem of queues from end-to-end delays, and efficient algorithms to identify the parameters associated with the same queue and infer the topology accordingly. The inferred topology is guaranteed to converge to the ground truth, up to a permutation of queues traversed by the same paths, as the number of measurements increases. Our evaluations validate the proposed solutions against benchmarks and identify potential directions for further improvements.

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U2 - 10.23919/IFIPNetworking52078.2021.9472774

DO - 10.23919/IFIPNetworking52078.2021.9472774

M3 - Conference contribution

AN - SCOPUS:85112801363

T3 - 2021 IFIP Networking Conference, IFIP Networking 2021

BT - 2021 IFIP Networking Conference, IFIP Networking 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th Annual IFIP Networking Conference, IFIP Networking 2021

Y2 - 21 June 2021 through 24 June 2021

ER -