TY - GEN
T1 - Predicting unexpected influxes of players in EVE online
AU - Garnett, Roman
AU - Gärtner, Thomas
AU - Ellersiek, Timothy
AU - Gudmondsson, Eyjólfur
AU - Óskarsson, Pétur
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - EVE Online is a massively multiplayer online roleplaying game (MMORPG) taking place in a large galaxy consisting of about 7 500 star systems. In comparison to many other online role-playing games, the users interact in the same instance of a persistent player-driven universe. Given the number of simultaneous pilots online at the same time - a number which at times reaches up to more than 50 000 concurrent accounts logged on to the same server - the EVE Online universe can present atypically difficult load-balancing challenges when the users decide to operate in a coordinated fashion, for example, to launch an attack on a particular system. We will present an scalable, automated statistical method for predicting such unexpected user gatherings by considering the evolving shortestpath distances from each user to each system. Here we present a case study analyzing nearly 300 million user movements in the EVE Online universe from over 700 thousand user accounts over a period of three months. We demonstrate an ability to predict sudden spikes in user presence (corresponding to actual events) before they happen, suggesting our techniques could be useful for automated load-balancing in such massive online games.
AB - EVE Online is a massively multiplayer online roleplaying game (MMORPG) taking place in a large galaxy consisting of about 7 500 star systems. In comparison to many other online role-playing games, the users interact in the same instance of a persistent player-driven universe. Given the number of simultaneous pilots online at the same time - a number which at times reaches up to more than 50 000 concurrent accounts logged on to the same server - the EVE Online universe can present atypically difficult load-balancing challenges when the users decide to operate in a coordinated fashion, for example, to launch an attack on a particular system. We will present an scalable, automated statistical method for predicting such unexpected user gatherings by considering the evolving shortestpath distances from each user to each system. Here we present a case study analyzing nearly 300 million user movements in the EVE Online universe from over 700 thousand user accounts over a period of three months. We demonstrate an ability to predict sudden spikes in user presence (corresponding to actual events) before they happen, suggesting our techniques could be useful for automated load-balancing in such massive online games.
UR - https://www.scopus.com/pages/publications/84910069665
U2 - 10.1109/CIG.2014.6932878
DO - 10.1109/CIG.2014.6932878
M3 - Conference contribution
AN - SCOPUS:84910069665
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - IEEE Conference on Computatonal Intelligence and Games, CIG
PB - IEEE Computer Society
T2 - 2014 IEEE Conference on Computational Intelligence and Games, CIG 2014
Y2 - 26 August 2014 through 29 August 2014
ER -