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

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


Edward Carlson, Tian Fan, Zijian Guan, Xiaokun Xu and Mark Claypool

In Proceedings of the Game Systems Workshop (GameSys)
Istanbul, Turkey
September 28 - October 1, 2021


Abstract

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.


Acknowledgments

Thanks to Google Stadia for supporting this work, Alejandra Garza, Joseph Swetz, Cameron Person, Adam Desveaux and James Plante for developing Catalyst, and Michael Bosik, Nina Taurich, and Alex Hunt for developing Nova.


Materials


See also: