  Social virtual reality learning environments (VRLEs) provide immersive
experience to users with increased accessibility to remote learning. Lack of
maintaining high-performance and secured data delivery in critical VRLE
application domains (e.g., military training, manufacturing) can disrupt
application functionality and induce cybersickness. In this paper, we present a
novel rule-based 3QS-adaptation framework that performs risk and cost aware
trade-off analysis to control cybersickness due to performance/security anomaly
events during a VRLE session. Our framework implementation in a social VRLE
viz., vSocial monitors performance/security anomaly events in network/session
data. In the event of an anomaly, the framework features rule-based adaptations
that are triggered by using various decision metrics. Based on our experimental
results, we demonstrate the effectiveness of our rule-based 3QS-adaptation
framework in reducing cybersickness levels, while maintaining application
functionality. Using our key findings, we enlist suitable practices for
addressing performance and security issues towards a more high-performing and
robust social VRLE.
