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Fine-Grained, Multi-Domain Network Resource Abstraction as a Fundamental Primitive to Enable High-Performance, Collaborative Data Sciences

Xiang, Qiao and Zhang, J. Jensen and Wang, X. Tony and Liu, Y. Jace and Guok, Chin and Le, Franck and MacAuley, John and Newman, Harvey and Yang, Y. Richard (2018) Fine-Grained, Multi-Domain Network Resource Abstraction as a Fundamental Primitive to Enable High-Performance, Collaborative Data Sciences. In: SIGCOMM '18 Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos. ACM , New York, NY, pp. 27-29. ISBN 9781450359153. http://resolver.caltech.edu/CaltechAUTHORS:20180813-091036914

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Abstract

Recently, a number of multi-domain network resource information and reservation systems have been developed and deployed, driven by the demand and substantial benefits of providing predictable network resources. A major lacking of such systems, however, is that they are based on coarse-grained or localized information, resulting in substantial inefficiencies. In this paper, we present Explorer, a simple, novel, highly efficient multi-domain network resource discovery system to provide fine-grained, global network resource information, to support high-performance, collaborative data sciences. The core component of Explorer is the use of linear inequalities, referred to as resource state abstraction (ReSA), as a compact, unifying representation of multi-domain network available bandwidth, which simplifies applications without exposing network details. We develop a ReSA obfuscating protocol and a proactive full-mesh ReSA discovery mechanism to ensure the privacy-preserving and scalability of Explorer. We fully implement Explorer and demonstrate its efficiency and efficacy through extensive experiments using real network topologies and traces.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3234200.3234208DOIArticle
ORCID:
AuthorORCID
Newman, Harvey0000-0003-0964-1480
Additional Information:© 2018 ACM. This research is supported in part by NSFC grants #61672385, #61472213 and #61702373; China Postdoctoral Science Foundation #2017-M611618; NSF grant CC-IIE #1440745; NSF award #1246133, ANSE; NSF award #1341024, CHOPIN; NSF award #1120138, US CMS Tier2; NSF award #1659403, SANDIE; DOE award #DE-AC02-07CH11359, SENSE; DOE/ASCR project #000219898; Google Research Award, and the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China61472213
National Natural Science Foundation of China61672385
National Natural Science Foundation of China61702373
China Postdoctoral Science Foundation2017-M611618
NSFOAC-1440745
NSFOAC-1246133
NSFOAC-1341024
NSFPHY-1120138
NSFOAC-1659403
Department of Energy (DOE)DE-AC02-07CH11359
Department of Energy (DOE)000219898
Google ResearchUNSPECIFIED
Army Research LaboratoryW911NF-16-3-0001
Ministry of Defence (UK)UNSPECIFIED
Subject Keywords:Interdomain, Collaborative Data Sciences, Resource Discovery, Resource State Abstraction
Record Number:CaltechAUTHORS:20180813-091036914
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180813-091036914
Official Citation:Qiao Xiang, J. Jensen Zhang, X. Tony Wang, Y. Jace Liu, Chin Guok, Franck Le, John MacAuley, Harvey Newman, and Y. Richard Yang. 2018. Fine-Grained, Multi-Domain Network Resource Abstraction as a Fundamental Primitive to Enable High-Performance, Collaborative Data Sciences. In Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos (SIGCOMM '18). ACM, New York, NY, USA, 27-29. DOI: https://doi.org/10.1145/3234200.3234208
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:88777
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:13 Aug 2018 21:28
Last Modified:05 Apr 2019 22:11

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