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The GENI Book pp 451-477 | Cite as

The Ignite Distributed Collaborative Scientific Visualization System

  • Matt Hemmings
  • Robert Krahn
  • David Lary
  • Rick McGeer
  • Glenn Ricart
  • Marko RöderEmail author
Chapter

Abstract

We describe the Ignite Distributed Collaborative Visualization System (IDCVS), a system which permits real-time interaction and visual collaboration around large data sets on thin devices for users distributed about the wide area. The IDCVS provides seamless interaction and immediate updates even under heavy load and when users are widely separated: the design goal was to fetch a 1 MB data set from a server and render it within 150 ms, for a user anywhere in the world, and reflect changes made by a user in one location to all other users within the bound given by inter-user network latency. Scientific collaboration and interaction is the initial use case for the IDCVS, since eScience is characterized by large data sets. The visualizer can be used for any application where the data can be visualized on a web page. The visualizer consists of many replicated components, distributed across the wide area, so that an instance of the visualizer is close to any user: the design goal is to place an instance of the visualizer with an 20-ms latency of any user. It is the first exemplar of a new class of application enabled by the Distributed Cloud: real-time interaction with large data sets on arbitrarily thin devices, anywhere. The IDCVS features modular design, so it functions as a specialized Platform-as-a-Service: writing a new collaborative visualization application is as simple as designing a web page and distributing a data server. The system was demonstrated successfully on a significant worldwide air pollution data set, with values on 10, 25, 50, and 100 km worldwide grids, monthly over an 18-year period. It was demonstrated on a wide variety of clients, including laptop, tablet, and smartphone. The system itself has been deployed at over 20 sites worldwide. Distribution and deployment across the GENI Experiment Engine was accomplished in 15 min, and installing a new site is limited by ftp time for the data set.

Keywords

Scientific Collaboration Visualization System Messaging System Internet Architecture Content Distribution Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors thank our colleagues at US Ignite, particularly Joe Kochan and William Wallace. We were assisted in deployment by the GENI Project Office, with particular thanks to Niky Riga, Mark Berman, Marshall Brinn, and Sarah Edwards. Andy Bavier set us up on the GEE, as did Rob Ricci on InstaGENI and Ilya Baldin on ExoGENI. Andi Bergen, Hausi Muller and Hadi Bannazadeh of the SAVI Project in Canada, Aki Nakao of the VNode project in Japan, Brecht Vermuelen of Fed4Fire, Max Ott of NICTA and Robert Hirschfeld of HPI set up our international sites. Joe Mambretti iCAIR set up international networking connections. We thank our demo participants—Wim van der Meer of iMinds, Tobias Pape of HPI, and Pratama Putra of the University of Tokyo. We thank our collaborators at the Lively Web, Dan Ingalls and Jens Lincke who participated in an early version of this chapter [9, 10]. Shushil Bhojwani collaborated with us on an early version of the data server, and Ulrike Stege and Yvonne Coady supported us throughout. This work was partially supported by the GENI Project Office, by SAP and by MITACS.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Matt Hemmings
    • 1
  • Robert Krahn
    • 4
  • David Lary
    • 2
  • Rick McGeer
    • 3
  • Glenn Ricart
    • 3
  • Marko Röder
    • 4
    Email author
  1. 1.Computer Sciences DepartmentUniversity of VictoriaVictoriaCanada
  2. 2.Department of PhysicsUniversity of Texas at DallasDallasUSA
  3. 3.US IgniteWashington, DCUSA
  4. 4.Y Combinator ResearchSan FranciscoUSA

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