Skip to Main content Skip to Navigation
Conference papers

Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses

Abstract : Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learning methods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with uniform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10 Gbps connections with 0–366 ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss rates but become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.
Complete list of metadata

https://hal.inria.fr/hal-03266451
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, June 21, 2021 - 5:31:07 PM
Last modification on : Friday, July 30, 2021 - 2:46:25 PM
Long-term archiving on: : Wednesday, September 22, 2021 - 7:00:35 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Nageswara Rao, Neena Imam, Zhengchun Liu, Rajkumar Kettimuthu, Ian Foster. Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses. 2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.154-174, ⟨10.1007/978-3-030-45778-5_11⟩. ⟨hal-03266451⟩

Share

Metrics

Record views

14