A Load Simulation and Metrics Framework for Distributed Virtual Reality

Abstract

We describe a simple load-measure-model method for analyzing the scalability of Distributed Virtual Environments (DVEs). We use a load simulator and three metrics to measure a DVE's engine with varying numbers of simulated users. Our load simulator logs in as a remote client and plays according to how users played during the conducted user study. Two quality of virtuality metrics, fidelity and consistency, describe the user's experience in the DVE. One engine performance metric provides the cycle time of the engine's primary loop. Simulation results (up to 420 users) are discussed.

Profiling Distributed Virtual Environments by Tracing Causality

“…Model checking requires that the system be accurately described in a modelling language, requiring a distinctive set of skills to those required to instrument code. However, a reliable model could reveal design defects before a system is implemented.Singh et al [24] used profiled metrics to validate and improve on models iteratively. They captured and coded real user data in order to construct a 'user simulator' for testing things such as per-user CPU and memory load on a DVE.…”

A methodology for managing distributed virtual environment scalability

“…With all the problems DVE system designers have to solve, a common subset is present throughout: synchronization and simulation Matković 2008, Singh andGračanin 2009). These activities are often significant components of a DVE engine's workload -driving significant parts of the resource requirements for operating the system.…”