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DTSTART;TZID=America/Chicago:20251117T113000
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UID:submissions.supercomputing.org_SC25_sess198_ws_pmbss110@linklings.com
SUMMARY:CGSim: A Simulation Framework for Large Scale Distributed Computin
 g Environment
DESCRIPTION:Sairam Sri Vatsavai (Brookhaven National Laboratory); Raees Kh
 an Ahmed (university of pittsburgh); Kuan-Chieh Hsu, Ozgur Kilic, Yihui (R
 ay) Ren, David Park, and Paul Nilsson (Brookhaven National Laboratory); Ta
 nia Korchuganova (University of Pittsburgh); Sankha Dutta (Brookhaven Nati
 onal Laboratory); Joseph Boudreau (University of Pittsburgh); Tasnuva Chow
 dhury (Brookhaven National Laboratory); Shengyu Feng (Carnegie Mellon Univ
 ersity); Fatih Furkan Akman (University of Massachusetts); Adolfy Hoisie (
 Brookhaven National Laboratory); Scott Klasky (Oak Ridge National Laborato
 ry (ORNL)); Tadashi Maeno (Brookhaven National Laboratory); Verena Ingrid 
 Martinez Outschoorn (University of Massachusetts); Norbert Podhorszki and 
 Frédéric Suter (Oak Ridge National Laboratory (ORNL)); John Rembrandt (Rem
 y) Steele (University of Massachusetts); Wei Yang (SLAC National Accelerat
 or Laboratory); Yiming Yang (Carnegie Mellon University); and Shinjae Yoo 
 and Alexei Klimentov (Brookhaven National Laboratory)\n\nLarge-scale distr
 ibuted computing infrastructures like the Worldwide LHC Computing Grid (WL
 CG) require comprehensive simulation tools for performance evaluation and 
 resource optimization. Existing simulators suffer from limited scalability
 , hardwired algorithms, lack of real-time monitoring, and inability to gen
 erate machine learning-suitable datasets.We present CGSim, a simulation fr
 amework addressing these limitations. Built on the validated SimGrid frame
 work, CGSim provides high-level abstractions for modeling heterogeneous gr
 id environments while maintaining accuracy and scalability. Key features i
 nclude a modular plugin mechanism for testing custom workflow policies, in
 teractive real-time visualization dashboards, and automatic generation of 
 event-level datasets for AI-assisted performance modeling. Comprehensive e
 valuation using production ATLAS PanDA workloads demonstrates significant 
 calibration accuracy improvements across WLCG sites. Scalability experimen
 ts show near-linear scaling for multi-site simulations, with distributed w
 orkloads achieving 6× better performance than single-site execution. CGSim
  enables researchers to simulate WLCG-scale infrastructures with hundreds 
 of sites and thousands of concurrent jobs on commodity hardware within pra
 ctical time budgets.\n\nRecording: Livestreamed, Recorded\n\nRegistration 
 Category: Technical Program Reg Pass, Workshop Reg Pass\n\nSession Chairs:
  Steven A. Wright (University of York, England); Simon Hammond (National N
 uclear Security Administration (NNSA)); and Sascha Hunold (Technical Unive
 rsity of Vienna)\n\n
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