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Software-Defined Network for End-to-end Networked Science at the Exascale

Monga, Inder and Guok, Chin and MacAuley, John and Sim, Alex and Newman, Harvey and Balcas, Justas and DeMar, Phil and Winkler, Linda and Lehman, Tom and Yang, Xi (2020) Software-Defined Network for End-to-end Networked Science at the Exascale. Future Generation Computer Systems, 110 . pp. 181-201. ISSN 0167-739X.

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Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

Item Type:Article
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URLURL TypeDescription
Newman, Harvey0000-0003-0964-1480
Additional Information:© 2020 Published by Elsevier B.V. Received 1 March 2019, Revised 26 February 2020, Accepted 8 April 2020, Available online 13 April 2020.
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0007346
Department of Energy (DOE)DE-SC0016585
Department of Energy (DOE)DE-AC02-07CH11359
Subject Keywords:Intent based networking; End-to-end orchestration; Intelligent network services; Distributed infrastructure; Resource modeling; Software defined networking; Real-time; Interactive
Record Number:CaltechAUTHORS:20200414-133611845
Persistent URL:
Official Citation:Inder Monga, Chin Guok, John MacAuley, Alex Sim, Harvey Newman, Justas Balcas, Phil DeMar, Linda Winkler, Tom Lehman, Xi Yang, Software-Defined Network for End-to-end Networked Science at the Exascale, Future Generation Computer Systems, Volume 110, 2020, Pages 181-201, ISSN 0167-739X, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102533
Deposited By: Tony Diaz
Deposited On:14 Apr 2020 20:44
Last Modified:22 Apr 2020 18:09

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