Abstract (EN):
Massive multiplayer online games (MMOGs) are
increasingly popular because they provide entertainment,
numerous opportunities for socialization, and the ability for users
to make money. Cornerstone to MMOGs is the underlying
network traffic between MMOG clients and servers;
understanding this traffic is important for application developers
trying to optimize game performance and for ISPs trying to
provide a better quality of service for their customers. This paper
goes a step forward in helping to understand MMOG network
traffic as it describes an automated approach to collect traffic
samples and map them to specific in-world actions in the MMOG
game Second LifeTM. We show the validity of our approach by
collecting 10 samples from 8 different actions, characterizing
these samples, and grouping samples by action using a k-means
clustering algorithm.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
3
License type: