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DTSTAMP:20260522T150110Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess326_spost104@linklings.com
SUMMARY:Geomancy: Automated Data Placement Optimization
DESCRIPTION:Oceane Bel (University of California, Santa Cruz)\n\nExascale 
 cloud storage and High-Performance Computing Systems (HPC) deliver unprece
 dented storage capacity and levels of computing power, though the full pot
 ential of these systems remain untapped because of inefficient data placem
 ent. Changes in data access patterns can cause a system's performance to s
 uffer. To mitigate performance losses, system designers implement strategi
 es to preemptively place popular data on higher performance nodes. However
 , these strategies fail to address a diverse userbase whose users individu
 ally demand the highest performance, and they must be carefully constructe
 d by an expert of the system.\n\nWe propose Geomancy, a tool that reorgani
 zes data to increase I/O throughput. In systems where heuristic-based impr
 ovements may become resource intensive, Geomancy determines new placement 
 policies by training a deep neural network with past workload and system t
 races. With real workload traces, Geomancy calculated an example placement
  policy that demonstrated a 49% increase in average throughput compared to
  the default data layout.\n\nRegistration Category: Tech Program Reg Pass,
  Exhibits Reg Pass\n\n
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