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Toward Fine-Grained, Privacy-Preserving, Efficient Multi-Domain Network Resource Discovery

Xiang, Qiao and Zhang, Jingxuan Jensen and Wang, Xin Tony and Liu, Yang Jace and Guok, Chin and Le, Franck and MacAuley, John and Newman, Harvey and Yang, Y. Richard (2019) Toward Fine-Grained, Privacy-Preserving, Efficient Multi-Domain Network Resource Discovery. IEEE Journal on Selected Areas in Communications, 37 (8). pp. 1924-1940. ISSN 0733-8716. https://resolver.caltech.edu/CaltechAUTHORS:20190711-143849459

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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 multiple properties of network resources ( e.g. , bandwidth, delay, and loss rate) 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. We implement a prototype Mercator and deploy it in a small federation network. We also evaluate the performance of Mercator through extensive experiments using real topologies and traces. Results show that Mercator 1) efficiently discovers available networking resources in collaborative networks on average four orders of magnitude faster, and allows fairer allocations of network resources; 2) preserves the member networks’ privacy with little overhead; and 3) scales to a collaborative network of 200 member networks.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/jsac.2019.2927073DOIArticle
http://resolver.caltech.edu/CaltechAUTHORS:20190813-104043961Related ItemConference Paper
ORCID:
AuthorORCID
Xiang, Qiao0000-0002-3394-6279
Zhang, Jingxuan Jensen0000-0002-1655-6121
Newman, Harvey0000-0003-0964-1480
Additional Information:© 2019 IEEE. Manuscript received December 15, 2018; revised June 27, 2019; accepted June 28, 2019. Date of publication July 5, 2019; date of current version August 6, 2019. This paper is an extended version of our paper “Fine-Grained, Multi-Domain Network Resource Abstraction as a Fundamental Primitive to Enable High-Performance, Collaborative Data Sciences” at ACM/IEEE Supercomputing 2018 [1]. This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. This work was also supported in part by NSF under Award 1440745, Award 1246133, Award 1341024, Award 1120138, and Award 1659403, in part by Department of Energy (DOE) under Award DE-AC02-07CH11359, in part by DOE/Advanced Scientific Computing Research (ASCR) Project under Grant 000219898, in part by DOE/ASCR under Award DE-SC00155527 and Award DE-SC0015528, and in part by the Google Research Award. The authors thank Haizhou Du, Kai Gao, Linghe Kong, Geng Li, Yeon-sup Lim, Alan Liu, Ennan Zhai, and Yan Zhu for their help during the preparation of this paper.
Funders:
Funding AgencyGrant Number
Ministry of Defence (UK)UNSPECIFIED
Army Research LaboratoryW911NF-16-3-0001
NSFOAC-1440745
NSFOAC-1246133
NSFOAC-1341024
NSFPHY-1120138
NSFOAC-1659403
Department of Energy (DOE)DE-AC02-07CH11359
Department of Energy (DOE)000219898
Department of Energy (DOE)DE-SC00155527
Department of Energy (DOE)DE-SC0015528
Google Research AwardUNSPECIFIED
Subject Keywords:Multi-domain networks, resource discovery, privacy preserving
Issue or Number:8
Record Number:CaltechAUTHORS:20190711-143849459
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190711-143849459
Official Citation:Q. Xiang et al., "Toward Fine-Grained, Privacy-Preserving, Efficient Multi-Domain Network Resource Discovery," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 8, pp. 1924-1940, Aug. 2019. doi: 10.1109/JSAC.2019.2927073
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97066
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:11 Jul 2019 21:58
Last Modified:03 Oct 2019 21:27

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