IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i11p393-d1507057.html
   My bibliography  Save this article

On Exploiting and Implementing Collaborative Virtual and Augmented Reality in a Cloud Continuum Scenario

Author

Listed:
  • Beniamino Di Martino

    (Department of Engineering, University of Campania "Luigi Vanvitelli", 81031 Aversa, Italy
    These authors contributed equally to this work.)

  • Gennaro Junior Pezzullo

    (Department of Engineering, University of Campania "Luigi Vanvitelli", 81031 Aversa, Italy
    These authors contributed equally to this work.)

  • Vincenzo Bombace

    (Department of Engineering, University of Campania "Luigi Vanvitelli", 81031 Aversa, Italy
    These authors contributed equally to this work.)

  • Ling-Huey Li

    (Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung 43301, Taiwan
    These authors contributed equally to this work.)

  • Kuan-Ching Li

    (Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung 43301, Taiwan
    These authors contributed equally to this work.)

Abstract

This work explores the application of collaborative virtual and augmented reality in a cloud continuum context, focusing on designing, implementing, and verifying three reference architectures for five collaborative VR/AR software deployment. The architectures designed differ in their distribution of computational load: one handles everything in the cloud, one balances the load between the cloud and the edge, and the last concentrates the load entirely on the edge. The design of the architectures was initially outlined through sequence and component diagrams and then implemented using the most appropriate technologies and frameworks. For each architecture, a specific application was developed and deployed on the various components of that architecture to test its proper functioning. Finally, the scenarios were simulated to be stressed with a significant number of users, employing tools such as Cloud Analyst to analyze performance and present well-defined and implemented reference architectures.

Suggested Citation

  • Beniamino Di Martino & Gennaro Junior Pezzullo & Vincenzo Bombace & Ling-Huey Li & Kuan-Ching Li, 2024. "On Exploiting and Implementing Collaborative Virtual and Augmented Reality in a Cloud Continuum Scenario," Future Internet, MDPI, vol. 16(11), pages 1-18, October.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:393-:d:1507057
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/11/393/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/11/393/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:393-:d:1507057. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.