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Diolkos: improving ethernet throughput through dynamic port selection

Bel, Oceane and Pata, Joosep and Vlimant, Jean-Roch and Tallent, Nathan and Balcas, Justas and Spiropulu, Maria (2021) Diolkos: improving ethernet throughput through dynamic port selection. In: Proceedings of the 18th ACM International Conference on Computing Frontiers. Association for Computing Machinery , New York, NY, pp. 83-92. ISBN 978-1-4503-8404-9.

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In large networked systems, a sudden increase in traffic could slowdown the network significantly, impacting network quality for multiple users. We present Diolkos, a system that leverages smart switches to dynamically re-reroute data flows in response to drops in performance. In contrast to other techniques, our tool predicts the future throughput at each port in a switch if a data flow were to be sent through it, and updates which port should be taken to maximize throughput. We use several techniques to predict network switch performance on a software defined network (SDN) mimicking topologies commonly found in datacenters. Experimentally, we demonstrate the effectiveness of choosing a port to send flows through based on predicted performance. We found that using a distributed predictive technique achieves a 24% improvement over using a traditional heuristic technique.

Item Type:Book Section
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URLURL TypeDescription
Vlimant, Jean-Roch0000-0002-9705-101X
Spiropulu, Maria0000-0001-8172-7081
Additional Information:© 2021 Copyright held by the owner/author(s). We thank our collaborators Wenji Wu, Soren Telfer and Michael Jensen for their help in reviewing this paper. We also thank NVIDIA Corporation for their donation of a TITAN Xp GPU used as part of the development of Diolkos. Part of this work was conducted at "iBanks", the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of "iBanks". We acknowledge support from Caltech’s Intelligent Quantum Networks and Technologies (INQNET) research program, AT&T’s Palo Alto Foundry and funding support from the U.S. Department of Energy’s (DOE) Office of Advanced Scientific Computing Research as part of "Integrated End-to-end Performance Prediction and Diagnosis." This work is partially supported by a DOE/HEP QuantISED program grant, QCCFP/Quantum Machine Learning and Quantum Computation Frameworks (QCCFP-QMLQCF) for HEP, Grant No. DE-SC0019219. This work is partially supported by the U.S. DOE, Office of Science, Office of High Energy Physics under Award No. DE-SC0011925 and DE-AC02-07CH11359.
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019219
Department of Energy (DOE)DE-SC0011925
Department of Energy (DOE)DE-AC02-07CH11359
Subject Keywords:Network port selection, smart switches, performance enhancement
Record Number:CaltechAUTHORS:20210518-092623444
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Official Citation:Oceane Bel, Joosep Pata, Jean-Roch Vlimant, Nathan Tallent, Justas Balcas, and Maria Spiropulu. 2021. Diolkos: improving ethernet throughput through dynamic port selection. In Proceedings of the 18th ACM International Conference on Computing Frontiers (CF '21). Association for Computing Machinery, New York, NY, USA, 83–92. DOI:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109169
Deposited By: Tony Diaz
Deposited On:19 May 2021 18:39
Last Modified:19 May 2021 18:39

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