Budget and User Feedback Control Strategy-Based PRMS Scenario Web Application

  • Rui Wu
  • Jose Painumkal
  • Sergiu M. Dascalu
  • Frederick C. HarrisJr.
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


The Precipitation-Runoff Modeling System (PRMS) is used to study and simulate hydrological environment systems. It is common for an environmental scientist to execute hundreds of PRMS model runs to learn different scenarios in a study field. If the study case is complex, this procedure can be very time-consuming. Also, it is very hard to create different scenarios without an efficient method. In this paper, we propose a PRMS scenario web application. It can execute multiple model runs in parallel and automatically rent extra servers based on needs. The control strategy introduced in the paper guarantees that the expense is within the planned budget and can remind a system manager if the quantified user feedback score crosses the predefined threshold. The application has user-friendly interfaces and any user can create and execute different PRMS model scenarios by simply clicking buttons. The application can support other environmental models besides PRMS by filling the blueprint file.


PRMS Budget control User feedback Web application 



This material is based upon work supported by the National Science Foundation under grant numbers IIA-1329469 and IIA-1301726. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.


  1. 1.
    R.N. Calheiros, R. Ranjan, R. Buyya, Virtual machine provisioning based on analytical performance and QoS in cloud computing environments, in Proceedings of the 2011 International Conference on Parallel Processing. ICPP ’11 (IEEE Computer Society, Washington, DC, 2011), pp. 295–304Google Scholar
  2. 2.
    Docker, Docker - build, ship and run any app. Accessed 18 July 2017
  3. 3.
    Docker, Docker Swarm — Docker. Accessed 18 July 2017
  4. 4.
    M.M. Hossain et al., Web-service framework for environmental models, in Seventh International Conference on Internet Technologies & Applications (ITA) (IEEE, New York, 2017)Google Scholar
  5. 5.
    M. Hossain et al., Becoming dataone tier-4 member node: steps taken by the nevada research data center, in 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP)(IEEE, New York, 2017), pp. 1089–1094Google Scholar
  6. 6.
    In Consortium of universities for the advancement of hydrologic science. CUASHI. Accessed 18 July 2017
  7. 7.
    S. Karlin, J. McGregor, Many server queueing processes with Poisson input and exponential service times. Pacific J. Math. 8(1), 87–118 (1958)Google Scholar
  8. 8.
    G.H. Leavesley et al., Precipitaion-runoff modeling system: user’s manual. Water-Resources Investigations Report (1983), pp. 83–4238Google Scholar
  9. 9.
    S.L. Markstrom, R.G. Niswonger et al., GSFLOW – coupled ground-water and surface-water flow model based on the integration of the precipitation-runoff modeling system (PRMS) and the modular ground-water flow model. Water-Resources Investigations Report (2005)Google Scholar
  10. 10.
    S.L. Markstrom et al., The Precipitation-Runoff Modeling System. Version 4. U.S. Geological Survey Techniques and Methods, Book 6 (chap. B7) (Clarendon Press, Oxford, 2015), p. 158.
  11. 11.
    A. Mesos, Apache Mesos. Accessed 18 July 2017
  12. 12.
    Open Geospatial Consortium, OGC network common data form (netCDF) standards suite (2014)Google Scholar
  13. 13.
    L. Palathingal et al., Data processing toolset for the virtual watershed, in 2016 International Conference on Collaboration Technologies and Systems (CTS) (IEEE, New York, 2016), pp. 281–287Google Scholar
  14. 14.
    D.G. Tarboton et al., HydroShare: an online, collaborative environment for the sharing of hydrologic data and models (Invited). AGU Fall Meeting Abstracts (2013)Google Scholar
  15. 15.
    J. Wheeler, K. Benedict, Functional requirements specification for archival asset management: identification and integration of essential properties of services-oriented architecture products. J. Map Geogr. Libr. 11(2), 155–179 (2015)CrossRefGoogle Scholar
  16. 16.
    R. Wu, Virtual watershed platform. Accessed 18 May 2017
  17. 17.
    R. Wu et al., Self-managed elastic scale hybrid server using budget input and user feedback, in 12th FC: Workshop on Feedback Computing. ICAC 2017, Columbus, OH (2017)Google Scholar
  18. 18.
    Q. Zhu, G. Agrawal, Resource provisioning with budget constraints for adaptive applications in cloud environments, in Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (ACM, New York, 2010), pp. 304–307Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rui Wu
    • 1
  • Jose Painumkal
    • 1
  • Sergiu M. Dascalu
    • 1
  • Frederick C. HarrisJr.
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of NevadaRenoUSA

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