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DTSTAMP:20260724T151408Z
LOCATION:Bldg. 6 - 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:Access the recording\n\nOrganizer(s): Raees Ahmad Khan, Tatian
 a Korchuganova (University of Pittsburgh, CERN), and Alexei Klimentov (Bro
 okhaven National Laboratory, CERN)\n\nScientific advancement increasingly 
 relies on the ability to process, transfer, and analyze massive data strea
 ms in near real time across distributed and heterogeneous computing system
 s. Fields such as high energy physics, climate modeling, bioimaging, and m
 aterials science face growing demands from high-resolution imaging, sensor
 -rich experiments, and simulation-driven digital twins, all of which requi
 re workflows that are resilient, scalable, and low-latency. This session t
 ackles three core themes. First, resilient data management which addresses
  reliable movement, cataloging, and access of ever-growing datasets. Secon
 d, near–real-time workflows which explore low-latency streaming, analysis,
  and decision-making, highlighting strategies for heterogeneous architectu
 res. Third, AI-driven modeling and digital twins which enable predictive w
 orkflow optimization and co-design of next-generation infrastructures. By 
 connecting domain-specific challenges with generalizable solutions, the se
 ssion showcases how integrated, intelligent approaches empower scalable, f
 ault-tolerant scientific workflows and foster interdisciplinary collaborat
 ion, advancing the future of data-intensive discovery.\n\nData Management 
 and Data Streaming Services at Large Scale Experiments\n\nAs scientific ex
 periments scale toward the exabyte frontier, the primary bottleneck is shi
 fting from raw storage capacity to intelligent orchestration of data movem
 ent, cataloging and reliable access. In high-energy physics, the experimen
 ts at the Large Hadron Collider (LHC) manage over an exabyte o...\n\n\nTat
 iana Korchuganova (University of Pittsburgh)\n---------------------\nNear 
 Real Time Resilient Workflows\n\nIn this presentation, we advocate for inf
 ormation driven methodologies that construct adaptive surrogates for data 
 and workflow components. Rather than replicating entire pipelines, we sele
 ctively reduce data and computation based on quantified uncertainty and re
 levance. Central to this strategy is ...\n\n\nScott Klasky and Norbert Pod
 horszki (Oak Ridge National Laboratory)\n---------------------\nAI-Enabled
  Modeling, Simulation, and Optimization of Distributed Computing Systems\n
 \nDistributed computing infrastructures are growing in complexity and hete
 rogeneity, challenging traditional modeling and simulation methodologies. 
 Analytical approaches such as queueing-theoretic and Markov chain models, 
 while mathematically tractable, rely on simplifying assumptions that fail 
 to cap...\n\n\nSairam Sri Vatsavai (Brookhaven National Laboratory)\n-----
 ----------------\nPanel Discussion on Complex Workflows, Resilience, and D
 ata Management Challenges 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 s
 upport data intensive science. While this session considers solutions bein
 g developed for fields such as nuclear fusion, nuclear and hi...\n\n\nVere
 na Ingrid Martinez Outschoorn (University of Massachusetts Amherst, CERN)\
 n\nDomain: Engineering, Physics, Computational Methods and Applied Mathema
 tics\n\nSession Chairs: Raees Ahmad Khan (University of Pittsburgh, CERN) 
 and Tatiana Korchuganova (University of Pittsburgh, CERN)
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