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: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis. IEEE , Piscataway, NJ, Art. No. 5. ISBN 978-1-5386-8384-2. https://resolver.caltech.edu/CaltechAUTHORS:20181120-141045120
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Abstract
Multi-domain network resource reservation systems are being deployed, driven by the demand and substantial benefits of providing predictable network resources. However, a major lack of existing systems is their coarse granularity, due to the participating networks' concern of revealing sensitive information, which can result in substantial inefficiencies. This paper presents Mercator, a novel multi-domain network resource discovery system to provide fine-grained, global network resource information, for collaborative sciences. The foundation of Mercator is a resource abstraction through algebraic-expression enumeration (i.e., linear inequalities/equations), as a compact representation of the available bandwidth in multi-domain networks. In addition, we develop an obfuscating protocol, to address the privacy concerns by ensuring that no participant can associate the algebraic expressions with the corresponding member networks. We also introduce a super-set projection technique to increase Mercator's scalability. Finally, we implement Mercator and demonstrate both its efficiency and efficacy through extensive experiments using real topologies and traces.
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Additional Information: | © 2018 IEEE. The authors thank Kai Gao, Geng Li, Linghe Kong, Ennan Zhai, Alan Liu, Yeon-sup Lim and Haizhou Du for their help during the preparation of this paper. The authors also thank the anonymous reviewers for their valuable comments. This research is supported in part by NSFC grants #61702373, #61672385 and #61701347; China Postdoctoral Science Foundation #2017-M611618; NSF awards #1440745, #1246133, #1341024, #1120138, and #1659403; DOE award #DE-AC02-07CH11359; 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: | Multi-domain networks, resource discovery, privacy preserving | ||||||||||||||||||||||||||||||
Record Number: | CaltechAUTHORS:20181120-141045120 | ||||||||||||||||||||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20181120-141045120 | ||||||||||||||||||||||||||||||
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 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC '18). IEEE Press, Piscataway, NJ, USA, Article 5, 13 pages. | ||||||||||||||||||||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||||||||||||||||
ID Code: | 91092 | ||||||||||||||||||||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||||||||||||||||||||
Deposited By: | Tony Diaz | ||||||||||||||||||||||||||||||
Deposited On: | 09 Mar 2020 14:56 | ||||||||||||||||||||||||||||||
Last Modified: | 09 Mar 2020 13:19 |
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