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DTSTAMP:20181221T160729Z
LOCATION:C140/142
DTSTART;TZID=America/Chicago:20181113T110000
DTEND;TZID=America/Chicago:20181113T113000
UID:submissions.supercomputing.org_SC18_sess194_pap163@linklings.com
SUMMARY:Fine-Grained, Multi-Domain Network Resource Abstraction as a Funda
 mental Primitive to Enable High-Performance, Collaborative Data Sciences
DESCRIPTION:Paper\nArchitectures, Data Analytics, Networks, Tech Program R
 eg Pass\n\nFine-Grained, Multi-Domain Network Resource Abstraction as a Fu
 ndamental Primitive to Enable High-Performance, Collaborative Data Science
 s\n\nXiang, Zhang, Wang, Liu, Guok...\n\nMulti-domain network resource res
 ervation 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-d
 omain network resource discovery system to provide fine-grained, global ne
 twork resource information, for collaborative sciences. The foundation of 
 Mercator is a resource abstraction through algebraic-expression enumeratio
 n (i.e., linear inequalities/equations), as a compact representation of th
 e 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 correspondin
 g member networks. We also introduce a superset projection technique to in
 crease Mercator’s scalability. Finally, we implement Mercator and demonstr
 ate both its efficiency and efficacy through extensive experiments using r
 eal topologies and traces.
URL:https://sc18.supercomputing.org/presentation/?id=pap163&sess=sess194
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