Abstract
We investigate a novel approach to the use of jitter to infer network congestion using data collected by probes in access networks. We discovered a set of features in jitter and jitter dispersion —a jitter-derived time series we define in this paper—time series that are characteristic of periods of congestion. We leverage these concepts to create a jitter-based congestion inference framework that we call Jitterbug. We apply Jitterbug’s capabilities to a wide range of traffic scenarios and discover that Jitterbug can correctly identify both recurrent and one-off congestion events. We validate Jitterbug inferences against state-of-the-art autocorrelation-based inferences of recurrent congestion. We find that the two approaches have strong congruity in their inferences, but Jitterbug holds promise for detecting one-off as well as recurrent congestion. We identify several future directions for this research including leveraging ML/AI techniques to optimize performance and accuracy of this approach in operational settings.
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Notes
- 1.
Jitterbug repository: https://github.com/estcarisimo/jitterbug.
- 2.
We referred as near and far sides to consecutive IP pairs in a traceroute path following the convention defined by Luckie et al. [23].
- 3.
Implementation of Xuan et al. change point detection algorithm: https://github.com/hildensia/bayesian_changepoint_detection.
- 4.
One day has 96 periods of 15 min.
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Acknowledgement
We thank the anonymous reviewers for their insightful comments, and Maxime Mouchet for providing an implementation of the HMM algorithm. We would like to thank Fabian Bustamante (Northwestern University) for coming up with the original term Jitterbug to name this paper. This work was partly funded by research grants DARPA HR00112020014, NSF OAC-1724853 and NSF CNS-1925729.
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Carisimo, E., Mok, R.K.P., Clark, D.D., Claffy, K.C. (2022). Jitterbug: A New Framework for Jitter-Based Congestion Inference. In: Hohlfeld, O., Moura, G., Pelsser, C. (eds) Passive and Active Measurement. PAM 2022. Lecture Notes in Computer Science, vol 13210. Springer, Cham. https://doi.org/10.1007/978-3-030-98785-5_7
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