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DTSTAMP:20181221T160731Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181115T160000
DTEND;TZID=America/Chicago:20181115T163000
UID:submissions.supercomputing.org_SC18_sess186_pap521@linklings.com
SUMMARY:Stacker: An Autonomic Data Movement Engine for Extreme-Scale Data 
 Staging-Based In Situ Workflows
DESCRIPTION:Paper\nArchitectures, Data Management, File Systems, Networks,
  State of the Practice, System Software, Workflows, Tech Program Reg Pass\
 n\nStacker: An Autonomic Data Movement Engine for Extreme-Scale Data Stagi
 ng-Based In Situ Workflows\n\nSubedi, Davis, Duan, Klasky, Kolla...\n\nDat
 a staging and in situ workflows are being explored extensively as an appro
 ach to address data-related costs at very large scales. However, the impac
 t of emerging storage architectures (e.g., deep memory hierarchies and bur
 st buffers) upon data staging solutions remains a challenge. In this paper
 , we investigate how burst buffers can be effectively used by data staging
  solutions, for example, as a persistence storage tier of the memory hiera
 rchy. Furthermore, we use machine learning based prefetching techniques to
  move data between the storage levels in an autonomous manner. We also pre
 sent Stacker, a prototype of the proposed solutions implemented within the
  Data\-Spaces data staging service, and experimentally evaluate its perfor
 mance and scalability using the S3D combustion workflow on current leaders
 hip class platforms. Our experiments demonstrate that Stacker achieves low
  latency, high volume data-staging with low overhead as compared to in-mem
 ory staging services for production scientific workflows.
URL:https://sc18.supercomputing.org/presentation/?id=pap521&sess=sess186
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