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. https://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.
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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. | ||||||||||||||||||||||||||||||
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Subject Keywords: | Interdomain, Collaborative Data Sciences, Resource Discovery, Resource State Abstraction | ||||||||||||||||||||||||||||||
DOI: | 10.1145/3234200.3234208 | ||||||||||||||||||||||||||||||
Record Number: | CaltechAUTHORS:20180813-091036914 | ||||||||||||||||||||||||||||||
Persistent URL: | https://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: | 16 Nov 2021 00:29 |
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