Measurement and Analysis of a Massively Multiplayer
Online Role Playing Game Traffic
Jaecheol Kim
Eunsil Hong
Yanghee Choi
Seoul National University
Multimedia & Communications Lab
Seoul, Korea
+82-2-880-1832
Seoul National University
Multimedia & Communications Lab
Seoul, Korea
+82-2-880-1832
Seoul National University
Multimedia & Communications Lab
Seoul, Korea
+82-2-880-1832
[email protected]
[email protected]
[email protected]
ABSTRACT
This paper describes the measurement and analysis of a Massively
Multiplayer On-line Role Playing Game(MMORPG). About 3
billion packets were traced for around 8 days. The results showed
that MMORPG traffic has very small packet size and bursty interarrival time. By the analysis of per-flow inter-arrival time and
average connection time per flow, we showed the gamers’
behavior pattern. Tendency of bandwidth consumption during the
measurement period suggested a hint for the game server
management. Lastly, the correlation between number of users and
bandwidth showed linearity and this fact can be an important
reference information for anticipating future demands of game
servers and networks. Through the analysis of traced packets we
could build traffic models of MMORPG, especially for packet
size and inter-arrival time. The model can be used for game traffic
generator and for network simulations.
General Terms
Measurement
Keywords
MMORPG(Massively Multiplayer Online Roleplaying Game),
traffic, inter-arrival time, traffic model.
1. INTRODUCTION
With the advances of Internet infra structure, ratios of traffics are
also changing dynamically. Recently, Online Game Traffics are
growing rapidly according to their popularity and provisioning.
There are many kinds of Online Games. But in the aspect of
traffic measurement, the games of large bandwidth consumption
and large participants arouse our interests. MMORPG(Massively
Multiplayer Online Role Playing Game)s are those.
Measurement of the Internet traffics has been done on many sorts
of applications and some kinds of online games has been
measured[2][3]. But intensive measurement and analysis of
MMORPG traffic has not been conducted yet. Common
characteristics of online game traffics are small and highly
periodic UDP packets[2]. But most MMORPGs uses TCP packets
because of client server structure and connection management
convenience.
World 1st MMORPG and world largest MMORPG are serviced in
Korea. ‘Lineage’ is the latter one. Simultaneous participants in
this game has exceeded 300 thousands. It has over 2 million
registered users around the world. We have captured ‘Lineage’
traffic for 8 days and could store 281 Gbytes of raw data.
In section 2, the details of measured results and their analysis are
presented. In section 3, the traffic model for MMORPG is also
suggested.
2. CHARACTERIZATION OF MMORPG
TRAFFIC
2.1 Measurement Method
To measure the MMORPG traffic, we used ‘tcpdump’. This
Software tool shows timestamp, IP address, port number, data size,
TCP flag of a packet. It is operated by the support of
‘Libpcap(Protocol Capture Library)’ and its timestamp resolution
was 400 usecs in our system. Timestamp resolution is dependent
on operating system and its packet filtering functionality.
Our measuring machine was operated on LINUX and had 512MB
DDR RAM. CPU of the system was P4(Intel) with 2.4 Ghz clock
speed. We adopted Gigabit Netwokr Interface card for
compliance with the gigabit-switching system. Measuring
machine was connected to the optical gigabit-switch where the
game server was connected and we used port mirroring function
to minimize additional traffic load on the switching system.
Before the real measurement, we conducted the test measurement
to check the packet drop ratio. We monitored the test trace for 10
minutes and found out no packet drop. So, even if we extended
the test measurement period, packet drop ratio would have been
negligible
2.2 Packet Size
Table 1 shows the statistics about overall packet number and
packet size. Server packets are larger than the client packets
because they contain data of multiple clients. The smallest
packets have no data and have just 40 bytes of header. They are
pure control packets such as SYN, ACK and FIN.
Figure 1 shows the distribution of server packet size and Figure 2
shows cumulative distribution of server packet size. Hereafter,
packet size represents the pure data bytes excluding the header
bytes. Both figures are about server packets. As shown in the
figures, packet sizes are very small and are narrowly distributed.
This characteristic is similar with the results of counter-striker
game[2]. Even though the average packet sizes of MMORPG and
non-MMORPG are different, common point is their packet sizes
are very small. Distribution of client packet size also shows the
similar tendency in Figure 3 and Figure 4.
(Total : 480,853,113 )
100%
(18byte, 98%)
90%
80%
70%
60%
Table 1 Statistics about Packet Size
50%
Server Æ Client
Client Æ Server
Total Packet Number
1,640,323,336
1,860,209,597
Data Packet Number
1,562,883,785
480,853,113
40%
30%
20%
10%
Packet Size (bytes)
0%
0
Largest Packet Size
1,500
1,500
Smallest Packet Size
40
40
Average Size
76.73
49.04
Standard Deviation
58.60
2.52
100
200
300
400
500
600
700
800
900
1000
Figure 4 Cumulative Distribution of Client Packet Size
2.3 Inter-arrival & Inter-departure time
We analyzed the inter-arrival time of packets to server first.
Maximum inter-arrival time was 20 seconds and the minimum
was 0 second. Average value was 386 usec. In figure 5, we can
see that 90% of packets inter-arrive within 0.8 msec. and 99%
within 2 msec.
Packet Number ( x10^9)
5
(12byte, 486,334,369)
4
100%
(2ms, 99%)
(1ms, 93%)
90%
3
80%
70%
2
60%
50%
1
40%
Packet Size (bytes)
0
0
100
200
300
400
500
600
700
800
900
1000
30%
20%
10%
Figure 1 Distribution of Server Packet Size
time (msec)
0%
0
100%
2
3
4
5
6
7
8
9
10
Figure 5 Cumulative Inter-arrival Time
( 173byte, 98% )
(115byte, 95% )
90%
1
Inter-departure time from server has also similar values with
inter-arrival time. The average value is 438 usec. This is less
frequent than inter-arrival time. That means clients send packets
more frequently than server does. Figure 6 shows that 88% of
packets departed within 1msecs. and 99% within 4 msecs.
(78byte, 90%)
80%
70%
60%
50%
40%
30%
20%
100%
10%
Packet Size (bytes)
(2ms, 96%)
90%
0
200
400
600
800
1000
1200
(4ms, 99%)
(1ms, 88%)
0%
80%
70%
Figure 2 Cumulative Distribution of Server Packet Size
60%
50%
40%
30%
Packet Number ( x10^9 )
3
20%
(8byte, 300,753,367)
10%
Time (ms)
0%
2.5
0
2
1
2
3
4
5
6
7
8
9
10
Figure 6 Cumulative Inter-departure Time
1.5
(11, 113,107,642)
1
2.4 Inter-arrival time per flow
0.5
Packet Size (bytes)
0
0
100
200
300
400
500
600
700
800
900
Figure 3 Distribution of Client Packet Size
1000
In the previous section, we showed the inter-arrival time of
overall traffics. Here, we present one more inter-arrival time. That
is per-flow inter-arrival time. Flow means packet streams that
come and go from one specific client to the server. Per-flow inter-
arrival time designates a gamer’s behavior, especially his or her
action interval during the game. Figure 7 and 8 shows distribution
of per-flow inter-arrival time and cumulative distribution of it,
respectively.
Bandwidth drop points where the bandwidth falls near the bottom
are made by periodic system check and system anomalies.
server -> client
client -> server
(Mbytes/min)
22
Packet Number
( x10^6)
8-11 14:12
8-15 16:49
20
300
18
16
(200ms, 251,319,077)
250
14
12
200
10
8
150
6
4
8-11 6:25
100
2
8-9 9:38
8-15 8:56
8-9 20:17
8-15 15:14
0
8-8
8-9
12:00 0:00
50
8-9
8-10
12:00 0:00
8-10 8-11
12:00 0:00
8-11 8-12
12:00 0:00
8-12 8-13
12:00 0:00
8-13 8-14
12:00 0:00
8-14 8-15
12:00 0:00
8-15 8-16
12:00 0:00
(Time)
8-16
12:00
Inter-arrival time (msec)
0
0
100
200
300
400
500
600
700
800
900
Figure 9 Bandwidth Consumption During a Week
1000
Figure 7 Distribution of per-flow inter-arrival time
2.7 Correlation between number of users and
bandwidth
100%
(800ms, 98%)
90%
(400ms, 90%)
(277ms, 80%)
80%
70%
60%
50%
40%
30%
20%
10%
Inter-arrival time (msec)
0%
0
200
400
600
800
1000
1200
1400
1600
1800
Different on-line games have different correlation between
number of users and bandwidth according to their game structures.
In MMORPG like ‘Lineage’, what does the number of users have
to do with bandwidth? Just linear relation? Or exponential
relation? To clarify the correlation between them, we separated all
the data into segments of 1 minute duration. The number of users
and their traffic volume was calculated for each 1 minute segment.
Thus, we could get 11983 segments.
2000
Figure 8 Cumulative Distribution of per-flow inter-arrival
time
Peak point of per-flow inter-arrival time stood at 200ms. 98% of
per-flow inter-arrival time was shorter than 800ms. It implies that
a gamer sends at least a packet in a second. Average per-flow
inter-arrival time was 263.58 msec and it means that a gamer
sends approximately 4 packets per second to the server.
2.5 Average Connection Time Per Flow
Among 215,254 total flows, 161,648 flows lasted more than or
equal to 1 second. We counted these ones for the calculation of
average connection time because flows with shorter connection
time were not really connected to the server. Average connection
time of these really connected flows was 2980.846 seconds. This
shows that average playing time of a gamer is about 49.68
minutes.
Figure 10 shows the result. Here, we could find out that the
correlation between number of users and bandwidth is linear.
Some can argue about the result because when the number of
users increases, the interaction between game server and clients
can increase exponentially. For example, if there are 5 clients
connected to a server and a client is added, then a message from a
client should be broadcast to 5 other clients. In this way, if each
of 6 clients sends a message to the server at the same time, then
30(6*5) messages should be broadcast to every other clients. But
in case of ‘Lineage’, not every client’s message to the server is
broadcast. A client’s message is forwarded just to a small group
of other clients who are in the same area. So, the effect of user
increase is restricted to a small amount. This result can be a
reference information when the network and server of service
company is redesigned.
(Mbytes/min)
y = 19403x - 822367
R2 = 0.9756
server->client
client->server
server->client
client->server
20
15
2.6 Tendency of Bandwidth Consumption
During a Week
We analyzed the tendency of bandwidth consumption during the
measurement period. Figure 9 shows the periodicity of bandwidth
consumption. Between 11 a.m. and 10 p.m. every day, bandwidth
was consumed more than in another durations. Around 6:30 a.m.,
traffic was least. Peak points were found on holidays. 11th
Aughust was Sunday and 15th August was Korea Independent
Day. In the afternoon(2 ~ 4 p.m.) on those days, bandwidth
consumption curve was on its summit.
y = 13708x - 443324
R2 = 0.991
10
5
Number of users
0
0
200
400
600
800
1000
1200
Figure 10 Correlation between Number of Users and
Bandwidth
3. TRAFFIC MODEL
When we simulate network traffic, the first step is to make traffic
model. The traffic model needs two submodels. Packet size and
Inter-arrival time are them. Each of them has different
characteristics so the models of them are inevitably different.
There are several mathematic models that can be adopted as the
traffic model of MMORPG. Among them two models matched
best the real traffic.
3.1 Packet Size
The distribution of packet size best matches ‘Power Lognormal
Distribution’. Following equation is the distribution function of
‘Power Lognormal Distribution’.
f ( x, p, σ ) = (
p
xσ
)φ (
log x
σ
)(Φ (
− log x
σ
))
p −1
x, p, σ > 0
Here, φ is probability density function of standard normal
distribution and Φ is the cumulative distribution function of
standard normal distribution.
Cumulative distribution function
Distribution’ is as follows.
F ( x, p , σ ) = 1 − (Φ (
of
− log x
σ
‘Power
))
p
Lognormal
x, p, σ > 0
From two equations above, by adjusting σ and p values, we
could get the proper distribution curve similar to the one we
measured.
3.2 Inter-arrival Time
Inter-arrival time doesn’t match any of existing mathematic
models. But the most appropriate model was ‘Extreme Value
Distribution’.
f ( x) =
1
β
x−µ
e
β
x− µ
e
−e
−
F ( x) = e
Based on the above analyses, we could propose the traffic model
for MMORPG. Packet size distribution could be modeled by
‘Power Lognormal Distribution’ and inter-arrival time by
‘Extreme Value Distribution’
This research result is restricted in its generality because just one
game was measured. For the improvements of its generality, some
more measurements of other on-line games are needed.
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β
Here, µ is location parameter and β is scale parameter.
Cumulative distribution function of ‘Extreme Value Distribution’
is as follows.
−e
connection time per flow was analyzed to show the trend of
gamers’ behavior. For the convenience of service company’s
management affairs, tendency of bandwidth consumption during a
week was analyzed. Correlation between number of users and
bandwidth can be the reference information for future
restructuring of server and network facilities.
x− µ
β
4. CONCLUSION
On-line games in the Internet are getting more and more
popularity. Due to this tendency, bigger traffic loads are imposed
on the Internet. Among those on-line games, MMORPG has
special characteristics in the aspect of the size of simultaneous
participants and its traffic burstiness. So, we measured an
MMORPG and analyzed its characteristics and made an traffic
model for MMORPG.
First we analyzed the distribution of packet size and the overall
inter-arrival time and inter-arrival time per flow as well. Average
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