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DTSTAMP:20260421T090513Z
LOCATION:Bldg. 6 - Room 103
DTSTART;TZID=Europe/Stockholm:20260630T134500
DTEND;TZID=Europe/Stockholm:20260630T154500
UID:submissions.pasc-conference.org_PASC26_sess145@linklings.com
SUMMARY:MS3E - Complex Workflows, Resilience, and Data Management Challeng
 es for Large Scale Experiments
DESCRIPTION:Scientific advancement increasingly relies on the ability to p
 rocess, transfer, and analyze massive data streams in near real time acros
 s distributed and heterogeneous computing systems. Fields such as high ene
 rgy physics, climate modeling, bioimaging, and materials science face grow
 ing demands from high-resolution imaging, sensor-rich experiments, and sim
 ulation-driven digital twins, all of which require workflows that are resi
 lient, scalable, and low-latency. This session tackles three core themes. 
 First, resilient data management which addresses reliable movement, catalo
 ging, and access of ever-growing datasets. Second, near–real-time workflow
 s which explore low-latency streaming, analysis, and decision-making, high
 lighting strategies for heterogeneous architectures. Third, AI-driven mode
 ling and digital twins which enable predictive workflow optimization and c
 o-design of next-generation infrastructures. By connecting domain-specific
  challenges with generalizable solutions, the session showcases how integr
 ated, intelligent approaches empower scalable, fault-tolerant scientific w
 orkflows and foster interdisciplinary collaboration, advancing the future 
 of data-intensive discovery.\n\nPanel Discussion on Complex Workflows, Res
 ilience, and Data Management Challenges for Large Scale Experiments\n\nA p
 anel involving all the speakers in the session and a moderator would discu
 ss integrated approaches for building scalable, efficient and resilient wo
 rkflows that support data intensive science. While this session considers 
 solutions being developed for fields such as nuclear fusion, nuclear and h
 i...\n\n\nVerena Ingrid Martinez Outschoorn (University of Massachusetts A
 mherst, CERN)\n---------------------\nData management and data streaming s
 ervices at large scale experiments\n\nAs scientific experiments scale towa
 rd the exabyte frontier, the primary bottleneck is shifting from raw stora
 ge capacity to intelligent orchestration of data movement, cataloging and 
 reliable access. In high-energy physics, the experiments at the Large Hadr
 on Collider (LHC) manage over an exabyte o...\n\n\nTatiana Korchuganova (U
 niversity of Pittsburgh)\n---------------------\nAI-Enabled Modeling, Simu
 lation, and Optimization of Distributed Computing Systems\n\nDistributed c
 omputing infrastructures are growing in complexity and heterogeneity, chal
 lenging traditional modeling and simulation methodologies. Analytical appr
 oaches such as queueing-theoretic and Markov chain models, while mathemati
 cally tractable, rely on simplifying assumptions that fail to cap...\n\n\n
 Sairam Sri Vatsavai (Brookhaven National Laboratory)\n--------------------
 -\nNear Real Time Resilient Workflows\n\nIn this presentation, we advocate
  for information driven methodologies that construct adaptive surrogates f
 or data and workflow components. Rather than replicating entire pipelines,
  we selectively reduce data and computation based on quantified uncertaint
 y and relevance. Central to this strategy is ...\n\n\nScott Klasky (Oak Ri
 dge National Laboratory)\n\nDomain: Engineering, Physics, Computational Me
 thods and Applied Mathematics\n\nSession Chairs: Raees Ahmad Khan (Univers
 ity of Pittsburgh, CERN); Tatiana Korchuganova (University of Pittsburgh, 
 CERN); and Alexei Klimentov (Brookhaven National Laboratory)
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