Characterizing On

Characterizing On-line Games
C. Chambers, W.-C. Feng,
S. Sahu, S. Saha, D. Brandt
Presented by: Vladislav Perelman
JUB-2011
1
Outline
Motivation
Experimental Setup
Predicting Game Workload
Player Characterization
2
Motivation
Everybody likes games
Online gaming = Business
Maximize profit + Minimize cost
Player satisfaction
Provisioning resources
3
Setup
GameSpy trace (550 games):
Casual Games trace (110 games):
4
Setup
cs.mshmro.com trace:
EVE online trace:
Data includes games of different genres.
More than 2 years of gaming data.
Millions of connections and users.
Sampling data every 10 minutes.
5
Game Workload
On a large scale:
Highly diverse player populations –
hard to predict long-term behavior
6
Game Workload
On a log scale:
Casual Games
GameSpy
The distribution of popularity is similar to power-law distribution:
f  x=ax

7
Game Workload
Short-term behavior:
Half-Life players load
FFT of year's data samples
Figures demonstrate the cyclic nature of short-term gaming workloads
8
Game Workload
Short-term behavior:
Top 5 GameSpy games:
Instantaneous load changes between
identical points in time for
consecutive weeks closely fit a
t location-scale distribution:
Most week-to-week load variations are under 15%
It is feasible to model week-to-week load variations
9
Game Workload
Share servers workload?
Between games
With web servers
Player loads are very similar no matter what the game is
Workload depends on the time of the day for games and the web
Very limited benefit can be achieved by sharing servers
10
Game Workload
Diurnal geographic patterns
Regular daily activity
patterns tied to working
hours
Lack of overlap between
different geographic regions
High degree of
synchronization between loads
on games and web servers
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“ WAKE UP”
(easy) Question!
Name the Game!
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“ WAKE UP”
(easy) Question!
13
Know your players
Patience is a virtue?
Patience = willingness to
reconnect to the server
Hard to track due to “ Quick
Start” feature
Patience decreases
exponentially
Acceptable reconnections on cs.mshmro.com
Not unexpected at all
(YouTube 'angry german kid' to
see live proof)
14
Know your players
Hard to keep new players –
many quit right after joining
Game's ability to attract people decreases in time
Disbalance in new and old players' abilities worsens
the situation
15
Know your players
“ Your update is ready to be installed...”
Providing updates keeps
players interested...at
least in theory
No large impact on the
overall subscription
Slight increase in average
playing time per player
16
Know your players
Individual approach
Detect when the player is
losing interest
Decrease in number of
sessions as well as
duration of each session
Deliver new content to
keep the player from
quitting
17
“ WAKE UP”
(difficult) Question!
Name the Game!
18
“ WAKE UP”
(difficult) Question!
19
Looking back
Game Popularity follows a power law
Short-term game workloads are predictable
Game workloads are synchronized with each other
and with the web
Gamers are impatient
Player churn is increasing over time
Gamers reduce the amount of time spent playing
before quitting altogether
20
21
For those who are interested
Games, in the order of appearance:
EVE Online (title slide)
➢Counter Strike
➢Neverwinter Nights
➢Battlefield 1942
➢StarCraft
➢Call of Duty
➢FullTilt Poker
➢Need for Speed
➢Super Mario Bros.
➢
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