Resource Allocation Predictive Modeling To Optimize Virtual World Simulator Performance
Keywords
Distributed simulation; Predictive model; Simulation-based training; Vertical scaling; Virtual world
Abstract
Virtual world simulation for military training is an emerging domain. As such, detailed analysis is required to optimize the performance the simulators. Unfortunately, due to a lack of extensive virtual world performance analysis, simulator administrators often make arbitrary resource allocations to support their environments and training scenarios. In this paper, we provide a lightweight predictive model that will be used in an automated, dynamic resource allocation system in the popular three-dimensional open-sourced virtual world simulator OpenSimulator. Prior to this investigation, only OpenSimulator developers and users with extensive experience with the platform could manually load balance the server resources based on anticipated usage. Now, with the proposed system and its predictive model, the simulator advances towards having an automated mechanism to determine the minimal critical resources that are required to support a target number of concurrent users in the virtual world.
Publication Date
3-2-2016
Publication Title
Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Number of Pages
1215-1219
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICMLA.2015.161
Copyright Status
Unknown
Socpus ID
84969705952 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84969705952
STARS Citation
Mondesire, Sean; Maxwell, Douglas; Stevens, Jonathan; and Leis, Rebecca, "Resource Allocation Predictive Modeling To Optimize Virtual World Simulator Performance" (2016). Scopus Export 2015-2019. 4219.
https://stars.library.ucf.edu/scopus2015/4219