<span class="var-sub_title">Fine-Grained, Multi-Domain Network Resource Abstraction as a Fundamental Primitive to Enable High-Performance, Collaborative Data Sciences</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

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


Authors: Qiao Xiang (Yale University), J. Jensen Zhang (Tongji University), X. Tony Wang (Tongji University), Y. Jace Liu (Tongji University), Chin Guok (Lawrence Berkeley National Laboratory), Franck Le (IBM), John MacAuley (Lawrence Berkeley National Laboratory), Harvey Newman (California Institute of Technology), Y. Richard Yang (Yale University)

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 superset 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.


Presentation: file


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