Towards Usable Attribute Scaling for Latency Compensation in Cloud-based Games

Edward Carlson, Tian Fan, Zijian Guan, Xiaokun Xu and Mark Claypool. Towards Usable Attribute Scaling for Latency Compensation in Cloud-based Games, In Proceedings of the Game Systems Workshop (GameSys), Istanbul, Turkey, September 28 - October 1, 2021. Online at: http://www.cs.wpi.edu/~claypool/paper...

Cloud-based games have advantages in convenience over traditional computer games, but have the disadvantage of added latency from the thin client to the cloud-based server and back. This added latency has been shown to decrease player performance. New latency compensation techniques can help by scaling game attributes to make the game easier, exactly counteracting the difficulty added by the latency. We conduct a user study measuring attribute scaling for two games - a first-person shooter and a rhythm game - each having a different attribute scaling method: spatial and temporal. Data from the study shows a decrease in accuracy with an increase in latency and game difficulty, and an increase in accuracy with an increase in attribute scaling. More importantly, we derive a model from the data whereby a pre-determined accuracy can be chosen - say, by the game designer - and the model then outputs the scaling factor to meet that desired target accuracy.