Abstract
We propose a neural network-based prediction method for the future entity layout in massively multiplayer online games. Our service has the potential to timely foresee critical hot-spots in fast-paced First Person Shooter action games that saturate the game servers which no longer respond to user actions at the required rate. Using our service, proactive load balancing (and redistribution) actions can be triggered.
We show results based on a realistic simulation environment that demonstrate the advantages of our method compared to other conventional ones, especially due to its ability to adapt to different load patterns.
This work is funded by EU through IST-034601 edutain@grid and IST-004265 CoreGrid projects.
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Nae, V., Prodan, R., Fahringer, T. (2008). Neural Network-Based Load Prediction for Highly Dynamic Distributed Online Games. In: Luque, E., Margalef, T., Benítez, D. (eds) Euro-Par 2008 – Parallel Processing. Euro-Par 2008. Lecture Notes in Computer Science, vol 5168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85451-7_22
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DOI: https://doi.org/10.1007/978-3-540-85451-7_22
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