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LOCATION:D173
DTSTART;TZID=America/Chicago:20181111T140000
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UID:submissions.supercomputing.org_SC18_sess163_ws_works107@linklings.com
SUMMARY:Planner: Cost-efficient Execution Plans Placement for Uniform Stre
 am Analytics on Edge and Cloud
DESCRIPTION:Laurent Prosperi (ENS Paris-Saclay); Alexandru Costan and Pedr
 o Silva (IRISA, INSA Rennes); and Gabriel Antoniu (French Institute for Re
 search in Computer Science and Automation (INRIA))\n\nStream processing ap
 plications handle unbounded and continuous flows of data items which are g
 enerated from multiple geographically distributed sources. Two approaches 
 are commonly used for processing: cloud-based analytics and edge analytics
 . The first one routes the whole data set to the Cloud, incurring signific
 ant costs and late results from the high latency networks that are travers
 ed. The latter can give timely results but forces users to manually define
  which part of the computation should be executed on Edge and to interconn
 ect it with the remaining part executed in the Cloud, leading to sub-optim
 al placements. In this paper, we introduce Planner, a middleware for unifo
 rm and transparent stream processing across Edge and Cloud. Planner automa
 tically selects which parts of the execution graph will be executed at the
  Edge in order to minimize the network cost. Real-world micro-benchmarks s
 how that Planner reduces the network usage by 40% and the makespan (end-to
 -end processing time) by 15% compared to state-of-the-art.\n\nTag: Reprodu
 cibility, Scientific Computing, Scientific Workflows, Workflows, HPC, Data
  Intensive\n\nRegistration Category: Workshop Reg Pass\n\n
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