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DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost103@linklings.com
SUMMARY:DFS on a Diet: Enabling Reduction Schemes on Distributed File Syst
 ems
DESCRIPTION:Posters, Research Posters\n\nDFS on a Diet: Enabling Reduction
  Schemes on Distributed File Systems\n\nWidodo, Abe, Kato\n\nThe selection
  of data reduction schemes, crucial for data footprints on a distributed f
 ile system (DFS) and for transferring big data, is usually limited to the 
 schemes supported by the underlying platforms. If the platform's source co
 de is available, it might be possible to add user-favorite reduction schem
 es, but it requires expensive implementation costs or is virtually impossi
 ble. We propose a system design that links a DFS to reduction schemes and 
 enables them transparently to data processing applications. We implemented
  a framework within Hadoop DFS (HDFS) named Hadoop Data Reduction Framewor
 k (HDRF). The features of HDRF are: users can easily incorporate their fav
 orite schemes with reasonably restrained implementation costs, the selecti
 on is transparent to data processing applications, and experimental result
 s show HDRF has low processing and storage overhead and can halve the vani
 lla HDFS transfer time by using a more optimized application, without comp
 romising the compression ratio.\n\nRegistration Category: Tech Program Reg
  Pass, Exhibits Reg Pass
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