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DTSTART:19700308T020000
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DTSTAMP:20181221T160726Z
LOCATION:D173
DTSTART;TZID=America/Chicago:20181111T140000
DTEND;TZID=America/Chicago:20181111T142500
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:Workshop\nReproducibility, Scientific Computing, Scientific Wo
 rkflows, Workflows, Workshop Reg Pass, HPC, Data Intensive\n\nPlanner: Cos
 t-efficient Execution Plans Placement for Uniform Stream Analytics on Edge
  and Cloud\n\nProsperi, Costan, Silva, Antoniu\n\nStream processing applic
 ations handle unbounded and continuous flows of data items which are gener
 ated from multiple geographically distributed sources. Two approaches are 
 commonly used for processing: cloud-based analytics and edge analytics. Th
 e first one routes the whole data set to the Cloud, incurring significant 
 costs and late results from the high latency networks that are traversed. 
 The latter can give timely results but forces users to manually define whi
 ch part of the computation should be executed on Edge and to interconnect 
 it with the remaining part executed in the Cloud, leading to sub-optimal p
 lacements. In this paper, we introduce Planner, a middleware for uniform a
 nd transparent stream processing across Edge and Cloud. Planner automatica
 lly selects which parts of the execution graph will be executed at the Edg
 e in order to minimize the network cost. Real-world micro-benchmarks show 
 that Planner reduces the network usage by 40% and the makespan (end-to-end
  processing time) by 15% compared to state-of-the-art.
URL:https://sc18.supercomputing.org/presentation/?id=ws_works107&sess=sess
 163
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