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DTSTART;TZID=America/Denver:20231115T153000
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UID:submissions.supercomputing.org_SC23_sess181_pap567@linklings.com
SUMMARY:Data Flow Lifecycles for Optimizing Workflow Coordination
DESCRIPTION:Hyungro Lee and Luanzheng Guo (Pacific Northwest National Labo
 ratory (PNNL)), Meng Tang (Illinois Institute of Technology), Jesun Firoz 
 and Nathan Tallent (Pacific Northwest National Laboratory (PNNL)), and Ant
 hony Kougkas and Xian-He Sun (Illinois Institute of Technology)\n\nA criti
 cal performance challenge in distributed scientific workflows is coordinat
 ing tasks and data flows on distributed resources. To guide these decision
 s, this paper introduces data flow lifecycle anal- ysis. Workflows are com
 monly represented using directed acyclic graphs (DAGs). Data flow lifecycl
 es (DFL) enrich task DAGs with data objects and properties that describe d
 ata flow and how tasks interact with that flow. Lifecycles enable analysis
  from several important perspectives: task, data, and data flow. We descri
 be representation, measurement, analysis, visualization, and opportunity i
 dentification for DFLs. Our measurement is both distributed and scalable, 
 using space that is constant per data file. We use lifecycles and opportun
 ity analysis to reason about improved task placement and reduced data move
 ment for five scientific workflows with different characteristics. Case st
 udies show improvements of 15×, 1.9×, and 10–30×. Our work is implemented 
 in the DataLife tool.\n\nTag: Cloud Computing, Distributed Computing, Data
  Movement and Memory, Performance Measurement, Modeling, and Tools\n\nRegi
 stration Category: Tech Program Reg Pass\n\nReproducibility Badges: Artifa
 ct Available, Artifact Functional, Results Reproduced\n\nSession Chair: Ja
 y Lofstead (Sandia National Laboratories, University of New Mexico)\n\n
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