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DTSTAMP:20260605T154541Z
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:Organizer(s): Raees Ahmad Khan, Tatiana Korchuganova (Universi
 ty of Pittsburgh, CERN), and Alexei Klimentov (Brookhaven National Laborat
 ory, CERN)\n\nScientific advancement increasingly relies on the ability to
  process, transfer, and analyze massive data streams in near real time acr
 oss distributed and heterogeneous computing systems. Fields such as high e
 nergy physics, climate modeling, bioimaging, and materials science face gr
 owing demands from high-resolution imaging, sensor-rich experiments, and s
 imulation-driven digital twins, all of which require workflows that are re
 silient, scalable, and low-latency. This session tackles three core themes
 . First, resilient data management which addresses reliable movement, cata
 loging, and access of ever-growing datasets. Second, near–real-time workfl
 ows which explore low-latency streaming, analysis, and decision-making, hi
 ghlighting strategies for heterogeneous architectures. Third, AI-driven mo
 deling and digital twins which enable predictive workflow optimization and
  co-design of next-generation infrastructures. By connecting domain-specif
 ic challenges with generalizable solutions, the session showcases how inte
 grated, intelligent approaches empower scalable, fault-tolerant scientific
  workflows and foster interdisciplinary collaboration, advancing the futur
 e of data-intensive discovery.\n\nData Management and Data Streaming Servi
 ces at Large Scale Experiments\n\nAs scientific experiments scale toward t
 he exabyte frontier, the primary bottleneck is shifting from raw storage c
 apacity to intelligent orchestration of data movement, cataloging and reli
 able access. In high-energy physics, the experiments at the Large Hadron C
 ollider (LHC) manage over an exabyte o...\n\n\nTatiana Korchuganova (Unive
 rsity of Pittsburgh)\n---------------------\nAI-Enabled Modeling, Simulati
 on, and Optimization of Distributed Computing Systems\n\nDistributed compu
 ting infrastructures are growing in complexity and heterogeneity, challeng
 ing traditional modeling and simulation methodologies. Analytical approach
 es such as queueing-theoretic and Markov chain models, while mathematicall
 y tractable, rely on simplifying assumptions that fail to cap...\n\n\nSair
 am Sri Vatsavai (Brookhaven National Laboratory)\n---------------------\nN
 ear Real Time Resilient Workflows\n\nIn this presentation, we advocate for
  information driven methodologies that construct adaptive surrogates for d
 ata and workflow components. Rather than replicating entire pipelines, we 
 selectively reduce data and computation based on quantified uncertainty an
 d relevance. Central to this strategy is ...\n\n\nScott Klasky and Norbert
  Podhorszki (Oak Ridge National Laboratory)\n---------------------\nPanel 
 Discussion on Complex Workflows, Resilience, and Data Management Challenge
 s for Large Scale Experiments\n\nA panel involving all the speakers in the
  session and a moderator would discuss integrated approaches for building 
 scalable, efficient and resilient workflows that support data intensive sc
 ience. While this session considers solutions being developed for fields s
 uch as nuclear fusion, nuclear and hi...\n\n\nVerena Ingrid Martinez Outsc
 hoorn (University of Massachusetts Amherst, CERN)\n\nDomain: Engineering, 
 Physics, Computational Methods and Applied Mathematics\n\nSession Chairs: 
 Raees Ahmad Khan (University of Pittsburgh, CERN) and Tatiana Korchuganova
  (University of Pittsburgh, CERN)
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