In order to judge the feasibility of such an approach, it is important for game and hosting companies to understand how gamers and game workloads behave. Knowing the behavior of players, the predictability of workloads, and the potential for resource sharing between applications allows infrastructure to be tailored to the needs of games. While there has been a substantial amount of work characterizing web and peer-to-peer users and workloads [11,12], there is very little known about game players and workloads.
In order to provide insight into such issues, this paper examines several large traces of aggregate player populations of a collection of popular games as well as the individual player population of a busy game server. We present a detailed analysis of on-line game players and workloads that targets several key areas which are important to game and hosting providers including:
: One of the key issues
in providing a successful game is to understand how players connect
to servers and how long they play on them. By understanding what
players are willing to put up with, game and hosting companies can
tailor their infrastructure and content to maximize player
satisfaction. For example, one of the challenges with using
on-demand computing infrastructure for games is the latency
associated with re-purposing a server. It would thus be useful to
characterize how patient game players are in connecting to a game
before deploying such infrastructure. To this end, we characterize
individual player behavior of an extremely popular Counter-Strike
game server over a long period of time. Our results show that
gamers are an extremely difficult set of users to satisfy and that
unless game servers are properly set up and provisioned,
gamers quickly choose to go elsewhere.
Another problem in
hosting on-line games is determining the amount of hardware and
network bandwidth that is required. Hosting a game is an expensive
proposition, costing the game provider more than 30% of the
subscription fees in just hardware and bandwidth per
month [13]. Hosting is made all the more difficult by
variations of popularity as the game moves through its life cycle.
Game companies face the provisioning problem both in determining the
amount of resources to provide at launch time and in allocating
spare resources to support dynamic usage spikes and subscriber
growth. Characterizing the diversity and predictability of game
workloads allows companies to more accurately provision resources.
To this end, we examine the real-time aggregate game player
population of more than 550 on-line games, the most popular of which
are first-person shooters. Our results show that the popularity of
these games follows a distinct power law distribution making the
provisioning of resources at launch-time extremely difficult.
However, as games mature, their aggregate populations do become
predictable, allowing game and hosting companies to more easily
allocate
resources to meet demand.
With the advent of commercial
on-demand computing infrastructure, it is becoming possible to
statistically multiplex server resources across a range of diverse
applications, thus reducing the overall hardware costs required to
run them. In order for such shared infrastructure to provide any
savings, peak usage of applications must not coincide. To
characterize the amount of sharing benefit that is available, we
examine the usage behavior of a number of popular on-line games and
compare them against each other and against the usage behavior of
several large distributed web sites. As on-demand infrastructure is
distributed, we also examine the client load of a number of servers
based on geographic region. Our results show that usage behavior of
interactive applications follows strict, geographically-determined,
time-of-day patterns with limited opportunities for
resource sharing.
Section 2 describes the methodology behind our study. Section 3 analyzes properties of individual gamers. Section 4 describes trends of on-line gaming in aggregate. Section 5 evaluates the potential for multiplexing games and web traffic together, and Section 6 discusses our conclusions.