Real-time latency prediction for cloud gaming applications

Abstract

Cloud gaming represents a rapidly growing segment in the entertainment industry, allowing users to stream and interact with high-quality games over the Internet. However, the problem of maintaining a seamless gaming experience is inherent to minimizing user-perceived latency. In this paper, we present CLoud Application lAtency Prediction (CLAAP), a novel solution that, to tolerate challenged network conditions in gaming, predicts such latency via a Machine Learning (ML) model and forecasts future network evolution. The model, trained over diverse network conditions and gaming scenarios, can then update its parameters via a concept drift detection algorithm that suggests a re-training action, reducing the prediction error up to 21% with minimal overhead. We then integrate this network metrics predictor into a game state prediction to further tolerate network latency spikes even from the user perspective, who can continue playing even in adversarial conditions without session interruptions. The results suggest the potential of advanced predictive analytics in mitigating latency issues, thereby setting the stage for more responsive and immersive cloud gaming services.