A predictive self-configuring simulator for online media

Shuochao Yao, Yifan Hao, Dongxin Liu, Shengzhong Liu, James Flamino, Huajie Shao, Jiahao Wu, Mouna Bamba, Tarek Abdelzaher, Boleslaw Szymanski

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper describes the design, implementation, and early experiences with a novel agent-based simulator of online media streams, developed under DARPA's SocialSim Program to extract and predict trends in information dissemination on online media. A hallmark of the simulator is its self-configuring property. Instead of requiring initial set-up, the input to the simulator constitutes data traces collected from the medium to be simulated. The simulator automatically learns from the data such elements as the number of agents involved, the number of objects involved, and the rate of introduction of new agents and objects. It also develops behavior models of simulated agents and objects, and their dependencies. These models are then used to run simulations allowing for future extrapolations and “what if” analyses. Results are presented on using this system to simulate GitHub transactions. They show good performance in terms of both simulation accuracy and overhead.

Original languageEnglish (US)
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781538665725
StatePublished - Jul 2 2018
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2018 Winter Simulation Conference, WSC 2018

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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