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SDN for End-to-End Networked Science at the Exascale (SENSE)

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 (2018) SDN for End-to-End Networked Science at the Exascale (SENSE). In: 2018 IEEE/ACM Innovating the Network for Data-Intensive Science. IEEE , Piscataway, NJ, pp. 33-44. ISBN 9781728101941. https://resolver.caltech.edu/CaltechAUTHORS:20190424-093533000

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Abstract

The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of `big data' driven by Exascale, cloud computing, machine learning and AI. The project's architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive `intent' based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a first-class schedulable resource akin to instruments, compute, and storage, to enable well defined and highly tuned complex workflows that require close coupling of resources spread across a vast geographic footprint such as those used in science domains like high-energy physics and basic energy sciences.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/indis.2018.00007DOIArticle
ORCID:
AuthorORCID
Newman, Harvey0000-0003-0964-1480
Additional Information:© 2018 IEEE. We appreciate the contributions to Intent APIs and superfacility use case by Mariam Kiran and NERSC testbed deployment through Damian Hazen and Jason Lee. Work discussed in this paper was supported through multiple projects from Department of Energy and National Science Foundation projects including the following: Caltech: OLiMPS, DOE/ASCR, DOE award #DE-SC0007346; SDN-Next Generation Integrated Architecture (SDN-NGenIA), DOE/ASCR, DE-SC0015527; SDN for End-to-end Networked Science at the Exascale (SENSE), DOE/ASCR, DE-SC0015528; ANSE, NSF award # 1246133; CHOPIN, NSF award # 1341024; US CMS Tier2, NSF award # 1120138. University of Maryland: SDN for End-to-end Networked Science at the Exascale (SENSE), DOE/ASCR, DE-SC0016585; Resource Aware Intelligent Network Services (RAINS), DOE/ASCR, DE-SC0010716. Fermi National Accelerator: SDN for End-to-end Networked Science at the Exascale (SENSE), DOE/ASCR. Argonne National Lab: SDN for End-to-end Networked Science at the Exascale (SENSE), DOE/ASCR. Lawrence Berkeley National Lab/esnet: SDN for End-to-end Networked Science at the Exascale (SENSE), DOE/ASCR, FP00002494.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0007346
Department of Energy (DOE)DE-SC0015527
Department of Energy (DOE)DE-SC0015528
NSFOAC-1246133
NSFOAC-1341024
NSFPHY-1120138
Department of Energy (DOE)DE-SC0016585
Department of Energy (DOE)DE-SC0010716
Department of Energy (DOE)FP00002494
Subject Keywords:Intent based networking, multi-resource orchestration, intelligent network services, distributed infrastructure, resource modeling
Record Number:CaltechAUTHORS:20190424-093533000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190424-093533000
Official Citation:I. Monga et al., "SDN for End-to-End Networked Science at the Exascale (SENSE)," 2018 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), Dallas, TX, USA, 2018, pp. 33-44. doi: 10.1109/INDIS.2018.00007
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94913
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Apr 2019 17:29
Last Modified:03 Oct 2019 21:08

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