Observing Slow Crustal Movementin Residential User Trafﬁc(PaperID:1569138435 12pages)

Kenjiro Cho

Kensuke Fukuda

Hiroshi Esaki

Akira Kato

IIJ

NII

Univ. of Tokyo

Keio Univ.

ABSTRACT

larger in amount than traditional web content. Thus, it
is often projected that rapidly increasing video content
is leading to explosive growth in the Internet traﬃc.
Despite of this popular claim, technical sources report only modest traﬃc growth worldwide. Odlyzko
estimates the Internet traﬃc growth rate in 2007 to
be about 50% to 60% in the U.S. and worldwide[19].
Cisco’s recent report also estimates that worldwide Internet traﬃc growth has been around 50% per year overthe last few years[6].
The growth of the Internet traﬃc volume is one of
the key factors driving research, development and investment in data communication technologies and infrastructure. With the annual growth rate of 100%, it
grows 1000-fold in 10 years, while with 50%, it grows
only 58-fold. Hence, a diﬀerence in growth rate has aconsiderable impact in the long run. In order to accommodate innovations brought by empowered end users,
crucial is not just growth rate but the balance among
demands, technological advances and investment in infrastructure. If the growth is underestimated, we may
not have capacity enough to handle new demands. If
overestimated, investment in technologies and capacity
may be only wasted.
We have been studying commercial backbone traﬃcin Japan with support from six ISPs covering 42% of theJapanese backbone traﬃc. (We started with seven ISPs
but two were merged in 2006.) In our previous work[3],we reported that the backbone in 2005 was dominatedby residential user-to-user traﬃc.
Japan is one of the highest ﬁber access penetration
countries in the world[20], and the number of Fiber-ToThe-Home (FTTH) subscribers is about to exceed thatof DSL subscribers as shown in Figure 1[29]. Meanwhile, the total number of broadband subscribers hasreached 56% of households and increased only 5% in
2007[29]. However, even with the abundant access capacity, the traﬃc growth rate at Japanese major IXes
has remained under 50% for the last few years as depicted in Figure 2. The plot shows aggregated peak trafﬁc at major IXes, JPIX[15], JPNAP[16] and NSPIXP[18],and illustrates multiplicative growth of traﬃc where the

It is often argued that rapidly increasing video content alongwith the penetration of high-speed access is leading to explosive growth in the Internet trafﬁc. Contrary to this popular claim, technically solid reports show only modest trafﬁc
growth worldwide. This paper sheds light on the causes of
the apparently slow growth trends by analyzing commercial
residential trafﬁc in Japan where the ﬁber access rate is muchhigher than other countries. We ﬁrst report that Japanese residential trafﬁc also has modest growth rates using aggregatedmeasurements from six ISPs. Then, we investigate residential per-customer trafﬁc in one ISP by comparing trafﬁc in2005 and 2008, before and after the advent of YouTube andother similar services. Although at ﬁrst glance a small segment of peer-to-peer users still dictate the overall volume,
they are slightly decreasing in population share. Meanwhile,
the rest of the users are steadily moving towards rich media
content with increased diversity. Surely, a huge amount of
online data and abundant headroom in access capacity canconceivably lead to a massive trafﬁc growth at some point in
the future. The observed trends, however, suggest that video
content is unlikely to disastrously overﬂow the Internet, atleast not anytime soon.

Categories and Subject Descriptors
C.2.3 [Computer-Communication Networks]: Network Operations—Network monitoring

General Terms
Measurement, Management

Keywords
traﬃc growth, ISP traﬃc, residential broadband

1. INTRODUCTION
For the last few years, video content becomes increasingly popular among Internet users. Today’s Internet
users casually view and share video content, beneﬁted
from the penetration of broadband access and easy-touse video services on the Net. On the other hand, video
and other rich media content are by orders of magnitude
1

Number of subscribers [million]15

to-peer users still dictate the overall traﬃc volume, it isdecreasing in both population share and volume share.
At the same time, the rest of the users are steadily moving towards rich media content with increased diversity.
The current traﬃc is heavily aﬀected by an eruptionof peer-to-peer applications but the crust underneathis also slowly rising with video and other rich media
content. The crustal movement is slow at the macrolevel so that it is unlikely to cause a major quake in thenear future.

DSL
CATV
FTTH

10

5

0

2000 2001 2002 2003 2004 2005 2006 2007 2008
Year

2.

4

300

3
200

2
100
Traffic volume
Growth rate
02000 2001 2002 2003 2004 2005 2006 2007 2008
Year

RELATED WORKThis is a sequel of our previous work[3] in which wecompared traﬃc usage between ﬁber and DSL users,
and between heavy-hitters and normal users. The focus was on heavy-hitters in residential traﬃc but thispaper focuses on ordinary residential users. One of thecontributions of this paper is to illustrate the impacts ofpeer-to-peer and rich media traﬃc on the current traﬃc
growth. Another contribution is to quantify changes intraﬃc usage of ordinary residential users.
The traﬃc growth rate is reported to be slowing down
in recent years. Odlyzko monitors over 100 publicly
available traﬃc statistics, and estimates the Internet
traﬃc growth rate[19]: the U.S. Internet traﬃc grewat around 100% in early 1990, jumped up to 1,000% in
1995-1996 driven by world wide web and user expansion,
went back to 70-150% in 1997-2003, and settled down
to around 50% in 2004-2007. Cisco’s reports[6, 5] arebased on traﬃc data from its provider customers combined with analyst projections, and also estimates thatworldwide Internet traﬃc growth has been around 50%
per year over the last few years. Furthermore, Japanese
IX traﬃc data as shown in Figure 2 clearly showed thatthe growth rate (around 30%) is stable (i.e., multiplicative growth) over the last four years. These estimates
do not have details on user distributions so that our
detailed analyses are complementary to these reports.
There is little solid work in literature that tries tomeasure commercial residential traﬃc. Studies on ADSL
networks[23, 27] are similar to our per-customer analysis in monitoring access lines and comparing traﬃc volumes among data sets but their focus is on ﬁle-sharing
traﬃc. It is reported that the average daily traﬃc volume of heavy-hitters is 470MB for inbound and 760MB
for outbound, and that of non heavy-hitters is 9MB
for inbound and 27MB for outbound in 2006[27]. Another study on academic backbone traﬃc[14] reportsthat peer-to-peer traﬃc account for 86-93% of the total
traﬃc volume.
There are numerous studies on traditional web trafﬁc, peer-to-peer traﬃc and video traﬃc. Many of themtry to characterize traﬃc at the ﬂow level. Peer-to-peer
traﬃc is highly variable and skewed among participating nodes[24, 26, 12], and exhibit behavior considerably

Annual growth rate

Aggregated IX traffic [Gbps]

Figure 1: Number of residential broadband subscribers in Japan: 28.7 million total broadband
subscribers, 12.7 million for DSL, 12.2 million
for FTTH and 3.3 million for CATV as of March
2008.

1
0

Figure 2: Traﬃc growth of the aggregated peakrate at the major Japanese IXes

volume is doubling roughly every two years since 2005.
In this paper, we try to answer a key question: whatis the macro level impact of video and other rich media
content on the traﬃc growth at the moment? We shedlight on the causes of the apparently slow growth trend
by detailed analyses of commercial residential traﬃc.
We followed the methodology used in our previous
work[3], and used two diﬀerent data sets. The ﬁrst set
was collected by aggregating interface counters of edge
routers from the six ISPs. The other set was collectedby Sampled NetFlow[4] from one of the ISPs for detailed
per-customer analysis.
We ﬁrst report that Japanese residential traﬃc also
has a modest average annual growth rate of 27% overthe last three years by means of aggregated measurements from the six ISPs. Then, we further investigate
residential per-customer traﬃc in one of the ISPs bycomparing traﬃc in 2005 and 2008 that is before andafter the advent of YouTube and other similar video services. Although at ﬁrst glance a small segment of peer2

(B1)external 6IXes
JPNAP/JPIX/NSPIXP

diﬀerent from traditional web traﬃc[11, 1]. A recent
study on YouTube traﬃc[10] by monitoring an academic
network compares characteristics of video content withtraditional web content. The mean and median size ofvideo content are 10MB and 8MB, and the mean andmedian transfer rate is 394kbps and 328kbps. Anotherstudy[17] characterizes streaming media ﬁles stored online by crawling web sites, and compares diﬀerent typesof video content. Many studies report that ﬂow sizeand duration follow heavy-tailed distributions (e.g., [25,31, 2]), and discuss elephants and mice in ﬂows. Daily
traﬃc volume per user is the summation of ﬂow sizetransferred to a user but there exits few previous work
focusing on the evolution of traﬃc volume per user, especially for residential users.
Regarding a shift in traﬃc mix, Cisco’s report[6] hasstatistics about a traﬃc mix shift in consumer traﬃc;
peer-to-peer ﬁle-sharing traﬃc grew 29% in volume but
the volume share dropped from 60% in 2006 to 51% in
2007. The volume share of video content not including
peer-to-peer ﬁle-sharing traﬃc grew from 12% in 2006
to 22% in 2007, and is projected to be 50% in 2012. The
estimated peer-to-peer traﬃc share is smaller than our
results. The projection is calculated by projected online
hours per user multiplied by the average MB per hourfor diﬀerent traﬃc types. Our ﬁnding in traﬃc distributions could provide new insights into these traditional
projection methods.

(B2)
external domestic
local IXes
private peering/transit(B3)
external international

external provider edge

ISP
customer edge

(A1)
RBB customers
DSL/CATV/FTTH(A2)
non-RBB customers
leased lines
data centersdialup5 traffic groups at ISP cusomer and external boundaries

Figure 3: Five traﬃc groups for data collection
at ISP customer and external boundaries(A2) non-RBB customers represent customer lines
other than RBB customers, including leased lines,
data centers, and dialup lines. This group includes
RBB customers behind leased lines, e.g., secondor third level ISPs, since ISPs do not distinguishthem from other leased lines.
(B1) external 6IXes represent links for 6 major IXes,
namely JPIX, JPNAP and NSPIXP in both Tokyo
and Osaka in order to compare measurements atthese IXes as well as to know the traﬃc share of
our measurement.

3. AGGREGATED TRAFFIC ANALYSISThis section examines the traﬃc growth trends inJapan by means of aggregated measurements collectedfrom the six ISPs covering 42% of the Japanese backbone traﬃc. The results show that Japanese commercial traﬃc has modest growth rates over the last three
years. In particular, the average annual growth rate ofresidential traﬃc has been 27% from May 2005 throughMay 2008.

(B2) external domestic represents domestic external
provider links other than the 6IXes, including regional IXes, private peering and transit. We used
the term “domestic” to indicate that both ends
of a link are located in Japan. This group also
includes domestic peering with global ASes.

3.1 Measurement Methodology(B3) external international represents international
external provider links with one end point outsideof Japan.

The ISPs we collaborate with collect interface countervalues of almost all routers in their service networks
via SNMP, and archive per-interface traﬃc logs using
MRTG [22] or RRDtool [21]. Thus, it is possible for the
ISPs to provide aggregated traﬃc information if they
can classify router interfaces into a common set.
Our focus is on traﬃc crossing ISP boundaries which
can be roughly divided into customer traﬃc, and external provider traﬃc such as peering and transit. For
practical purposes, we selected the ﬁve traﬃc groups in
Figure 3 for data collection.

These groups are chosen by the existing operational
practices of the participating ISPs because it is impossible to draw a strict line for grouping, e.g., residential/business and domestic/international, on the global
Internet. We re-aggregate each ISP’s aggregated logs,
and only the resulting aggregated traﬃc is used in ourstudy so as to not reveal the share of each ISP. Although
the peak rate is often used for operational purposes,
only the mean rate is collected since the peak rate is
not summable.
In general, it is meaningless to simply sum up traﬃcvalues from multiple ISPs since a packet could cross
ISP boundaries multiple times. Customer traﬃc is,(A1) RBB customers represent residential broadband
customer lines. This group also includes small
business customers using residential broadband access.
3

400

however, summable because a packet crosses customer
edges only once in each direction, when entering thesource ISP and exiting the destination ISP. The numbers for external provider traﬃc are overestimated sincea packet could be counted multiple times if it travels
across intermediate ISPs. Nevertheless, the error shouldbe negligible in this particular result since most of the
ISPs in our data sets are peering, and thus, not providing transit to each other. Although one ISP is identiﬁed
to have double counts for the transit link, it is compensated in the results by data provided separately for the
double count adjustment.
Each ISP provided month-long traﬃc logs aggregatedfor each traﬃc group by a log aggregation tool we developed. This allows ISPs not to disclose the internal
structure of their network or unneeded details of theirtraﬃc. The ﬁnal results were obtained by aggregating
all traﬃc logs provided by the six ISPs.
The time resolution of the logs is 2 hours since it
was the highest common factor for month-long data in
MRTG and RRDtool. 2-hour boundaries in UTC fallon odd hours in Japanese Standard Time (UTC+9).
Throughout the paper, inbound and outbound are presented from the ISPs’ point of view.
The data for each month was separately collected,
and consistency such as each ISP’s share, diﬀerencesfrom the previous measurements, the coverage of the
IX traﬃc was examined. Then, the aggregated results
were provided to the ISPs so that each ISP can compareand check its own data against the aggregated results.
In addition, a face-to-face meeting with representatives
from the participating ISPs is held after each data collection to check and discuss the results.
Monthly traﬃc logs with two-hour resolution allow us
to identify major changes in each ISP’s traﬃc. Whensuch changes are found, we contact the ISP to conﬁrm
the cause of the change, e.g., a network reconﬁguration,an outage, missing SNMP data, or a mis-classiﬁcationof interface counter logs. Afterwards, if necessary, we
ask the ISP for corrected data.

Traffic (Gbps)

300

A1(in)
A1(out)A2(in)
A2(out)200
100
0

2004/09 2005/05

2006/05

2007/05

2008/05

Traffic (Gbps)

150

100
B1(in)
B1(out)B2(in)
B2(out)B3(in)
B3(out)5002004/09 2005/05

2006/05

2007/05

2008/05

Figure 4: Traﬃc growth: customer traﬃc (upper) and external provider traﬃc (bottom)

Figure 5: Residential broadband customer trafﬁc in May 2005 (upper) and May 2008 (bottom)

3.2 Data Sets
We analyzed month-long traﬃc logs from six major Japanese ISPs over ﬁve years; September, October, November in 2004, May and November from 2005
through 2008. After the initial trials over three months,
we decided to collect data only twice a year to reduce
the workload of the participating ISPs.

for outbound, and 27% for the combined volume. The
diﬀerence between inbound and outbound has widened
over the years.
Figure 5 compares weekly RBB customer traﬃc (A1)in May 2005 and in May 2008. For weekly data analysis,
we took the averages of the same weekdays in a month.
We excluded holidays from the weekly analysis sinceholiday traﬃc patterns are closer to those of weekends.
The inbound and outbound traﬃc volumes were almost
equal in 2005, and about 120Gbps is constantly ﬂowing
in both directions, probably due to peer-to-peer appli-

3.3 Growth of Trafﬁc
The monthly average rates in bits/second of the trafﬁc groups are shown in Tables 1 and Figure 4. RBB customers (A1) consist of DSL/FTTH/CATV residential
users. Between May 2005 and May 2008, the averageannual growth rate of (A1) was 26% for inbound, 28%4

Table 1: Monthly average rates of aggregated customer and external traﬃc (bps)

2004

2005
2006
2007
2008

Sep
Oct
Nov
May
Nov
May
Nov
May
Nov
May

(A1)customer-RBB (A2)customer-non-RBB(B1)ext-6ix(B2)ext-dom(B3)ext-intl(6 ISPs)(4 ISPs)(6 ISPs)(6 ISPs)(6 ISPs)
inbound outbound inboundoutbound inbound outbound inbound outbound inbound outbound
98.1G
111.8G
14.0G
13.6G
35.9G
30.9G
48.2G
37.8G
25.3G
14.1G
108.3G
124.9G
15.0G
14.9G
36.3G
31.8G
53.1G
41.6G
27.7G
15.4G
116.0G
133.0G
16.2G
15.6G
38.0G
33.0G
55.1G
43.3G
28.5G
16.7G
134.5G
178.3G
23.7G
23.9G
47.9G
41.6G
73.3G
58.4G
40.1G
24.1G
146.7G
194.2G
36.1G
29.7G
54.0G
48.1G
80.9G
68.1G
57.1G
39.8G
173.0G
226.2G
42.9G
38.3G
66.2G
60.1G
94.9G
77.6G
68.5G
47.8G
194.5G
264.2G
50.7G
46.7G
68.4G
62.3G 107.6G
90.5G
94.5G
57.8G
217.3G
306.0G
73.8G
57.8G
77.4G
70.8G 124.5G
108.4G 116.4G
71.2G
237.2G
339.8G
85.4G
63.2G
93.5G
83.4G 129.0G
113.3G 133.7G
81.8G
269.0G
374.7G 107.0G
85.0G
95.7G
88.3G 141.2G
119.4G 152.6G
94.4G

cations which generate traﬃc independent of daily user
activities. The ratio between the peak and bottom (i.e.,
constant ﬂows) of the traﬃc volume decreased from 0.63
to 0.56 for inbound, and from 0.59 to 0.43 for outbound.
The diurnal pattern reﬂects home user activities, i.e.,
the traﬃc increases in the evening, and the peak hours
are from 21:00 to 23:00. Weekends can be identiﬁed bylarger daytime traﬃc although the peak rates are similar to weekdays. The outbound traﬃc to customers isslightly larger than the inbound in 2005, and becomesmuch larger in 2008 with larger ﬂuctuations and withclearer peaks. We believe that peer-to-peer applications
contribute signiﬁcantly to the upstream traﬃc from residential users.
The data for non-RBB customer traﬃc (A2) was obtained only from four ISPs; it is diﬃcult for the other
ISPs to distinguish external links from other links due
to historical reasons. Since (A2) from these other ISPs
is missing, it is not possible to directly compare (A1)with (A2). Hence, we estimated the ratio of (A1) to(A) using only data from the four ISPs with both (A1)and (A2). The estimated ratio (A1)/(A1+A2) was 59%
for inbound and 64% for outbound in November 2005,
and was 43% for inbound and 56% for outbound in May
2008. The ratio has decreased, especially for inbound,probably due to a decrease in peer-to-peer traﬃc share.
The total volume of external domestic traﬃc (B2),mainly private peering, exceeds the volume for the six
major IXes (B1) and the diﬀerence is widening, probably because major ISPs are shifting from public peering to private peering to handle increasing demands.
This result underlines a possible deviation in estimating nation-wide traﬃc only from IX traﬃc. However,
it is possible that the ratio of private peering is overestimated in our results as private peering is usually
exercised only between large ISPs.
Another noticeable change is the increase in international traﬃc, especially for inbound. This is probably
due to popular video services such as YouTube which
do not have servers in Japan.
We examined the relationship between our IX traﬃcdata (B1) and the total input rate of the six major IXes,
as obtained directly from these IXes [29]. In comparison

Table 2: Share of (B1) outbound to the IXes
against the inbound measured on the IX side
(%)
2004
2005
2006
2007
2008
Sep Oct Nov May Nov May Nov May Nov May
41.5 41.8 41.7 42.0 41.5 43.2 41.5 42.4 41.8 42.6

with the published total incoming traﬃc of these IXes,
our data consistently represents about 42% of the total
traﬃc as shown in Table 2. If we assume this ratio tobe the traﬃc share of the six ISPs, the total amount ofresidential broadband traﬃc in Japan in May 2008 canroughly be estimated to be 632Gbps for inbound and880Gbps for outbound, or 205PB/month for inboundand 285PB/month for outbound. These numbers arein line with Cisco’s estimates of 226PB/month for consumer traﬃc in Japan in 2008[6].
In summary, by comparing traﬃc in 2008 with 2005,
we observed the larger download volume as well as larger
evening volume in the residential traﬃc, the decreased
share of residential traﬃc in the customer traﬃc, andthe increased inbound volume in the international trafﬁc. All of these, along with the volume comparable insize to peer-to-peer traﬃc, indicate an increase in video
service content but the growth has been modest. A
plausible explanation is that residential traﬃc is shifting from peer-to-peer ﬁle-sharing to video services. We
will examine this hypothesis in the next section using
per-customer measurements from one of the ISPs.

4.

PER-CUSTOMER TRAFFIC ANALYSIS

Sets of Sampled NetFlow data in 2005 and 2008 wereobtained from one of the participating ISPs to further
analyze the behavior of residential traﬃc. Here, only
residential broadband customer traﬃc is analyzed. In
the data, one end of a ﬂow is always the residential customer of the ISP but the other end is generally a customer of another ISP. This ISP has residential broadband customers over DSL and ﬁber but not over CATV.
By comparing the aggregated residential traﬃc graphs
in Figure 5 with the ISP’s corresponding graphs, thetraﬃc characteristics are consistent. Hence, we believe5

traﬃc is the average for the week, and the distribution
is computed independently for inbound and outboundtraﬃc. The vertical lines in the plots are at 100MB/day
and 2.5GB/day.
When we look at the ﬁber users in 2005 (top middle), there appears to be two roughly lognormal distributions where the logarithm of the variable is normally
distributed. It is less clear in the other plots but the
hypothesis still holds.
The one group on the left includes majority of users,
and the outbound volume (download for users) is about
ten times larger than the inbound volume. The othergroup on the right is heavy-hitters with symmetric inbound and outbound volumes. The two distinct distributions suggest that they have diﬀerent mechanisms;
most likely, the symmetric high-volume distribution isdriven by the symmetric and aggressive nature of peerto-peer ﬁle-sharing. For convenience, we call this grouppeer-type, and the other group client-type. The two
distributions overlap, and the range of the overlap increased in 2008, especially for outbound. The spikes atthe left edge are due to the increased sampling rates.
The distribution of the client-type group has moved
towards higher volume, especially for outbound. The
mode for inbound shifted from 3.5MB/day in 2005 to
5MB/day in 2008, and the mode for outbound shiftedfrom 32MB/day in 2005 to 94MB/day in 2008. The
modes of the distributions are similar in both ﬁber and
DSL plots. The distribution of the outbound volume
becomes wider, suggesting increased diversity.
On the other hand, the distribution of the peer-typegroup is not growing from 2005 to 2008. The modeof the distribution stays at around 2GB/day, and thecovered area has slightly decreased. In fact, if we subtract the overlap of the client-type users from the plots,
the population share and volume share of the peer-typeusers slightly decreased. The outbound distribution isalmost absorbed by the client-type distribution, and becomes harder to distinguish in the 2008 plots. When focused on the tail at the right edge, the outbound volumeslightly grew but the inbound volume did not.
The corresponding (complementary) cumulative distributions of daily traﬃc per user for the total users in2005 and 2008 are shown on a log-log scale in Figure 7.
The distributions are heavy tailed but there is a kneein the slope, at the top 4% of heavy-hitters using morethan 2.5GB/day (or 230kbits/sec) for the total users in
2005. The distribution for inbound did not change muchin 2008 but that for outbound has moved to the right,
suggesting visible growth only in the download volume.
The inbound and outbound volumes of the ﬁber heavyhitters become symmetric, probably because the majority of the heavy-hitters become ﬁber users so that they
do not need to compensate for the shortage of upstream
bandwidth of DSL heavy-hitters.

that the results represent Japanese residential traﬃceven though the data sets are from only one ISP.

4.1 Measurement Methodology
Data was collected from all edge routers accommodating residential broadband customers. Traﬃc volume
is derived by dividing the measured volume by the sampling rate. In 2005, the sampling rate was 1/2048 unanimously but, in 2008, 45% of the routers, notably busy
ones, have 1/8192, 45% have 1/2048, and 10% have
1/1024, adjusted according to the load the routers. We
believe it is still enough for analyzing user behaviors but
there is a certain amount of sampling errors, especiallyfor lightweight users.

4.2 Data Sets
Two types of week-long data sets are used for analysis. One type is daily inbound and outbound traﬃc volume of each customer obtained by matching customer
IDs with the assigned IP addresses. This data also includes customer’s line type (DSL or ﬁber), and is used
to analyze per-customer behavior in Section 4.2 through4.4. The data was collected in February 21-27 in 2005
and June 2-8 in 2008. The other type is raw NetFlow
data, and used to analyze protocol usage in Section 4.5.
The data was collected in July 4-10 in 2005 and June
2-8 in 2008.
By comparing the 2008 data with the 2005 data,
the total number of active users increased by 94.7%,
and the total traﬃc volume increased by 187.0%. The
growth rate of the total traﬃc volume is larger than
the aggregated result, probably because the ISP has expanded business in the residential broadband services.
The daily traﬃc volume per active user increased from
430MB to 483MB for inbound by 12.3% and from 447MB
to 797MB for outbound by 78.3%; a signiﬁcant diﬀerence between inbound and outbound.
Table 3 shows the number of active unique users forﬁber and DSL in 2005 and 2008. The ﬁber user ratio
increased from 46% in 2005 to 79% in 2008 so that alarge majority of the active users become ﬁber users.
Accordingly, the total volume share of the ﬁber users
increased from 79% in 2005 to 87% in 2008.
Table 3: Ratio of ﬁber and DSL active users andtotal traﬃc volumes
2005 ﬁber
DSL
2008 ﬁber
DSL

active users (%) total volume (%)
46
79
54
21
79
87
21
13

4.3 Distribution of per-customer trafﬁc
Figure 6 shows the probability density functions ofdaily traﬃc per customer in log-linear scale. The daily
6

0.4
0.3
0.2
0.1

0.4
0.3
0.2

6

7

8

9

10
10
10
10
Daily traffic per user (bytes)

1010(d) Total (2008)

0 4
10

11

10

5

10

6

7

8

9

10

10
10
10
10
Daily traffic per user (bytes)

10(e) Fiber (2008)

In
Out

0.4
0.3
0.2
0.1
5

10

6

7

8

9

10
10
10
10
Daily traffic per user (bytes)

10

10

11

10

0.2

6

7

8

9

10
10
10
10
Daily traffic per user (bytes)

10

0.3
0.2

11

10

(f) DSL (2008)

0.4

0 4
10

5

10

10

In
Out

0.5

0.1

0 4
10

0.3

0 4
10

11

10

In
Out

0.5
Probability density

0.5

0.4

0.1

Probability density

5

10

In
Out

0.5

0.1

0 4
10

Probability density(c) DSL (2005)

In
Out

0.5
Probability density

Probability density(b) Fiber (2005)

In
Out

Probability density(a) Total (2005)
0.5

0.4
0.3
0.2
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Daily traffic per user (bytes)

10

11

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10

Figure 6: Probability density function of daily traﬃc per user: total (left), ﬁber users (middle), andDSL users (bottom) in 2005 (top) and 2008 (bottom).

1

0

10

In
Out

0.8
Cumulative traffic

Cumulative distribution

-1

10

-2

10

In (2005)
Out (2005)
In (2008)
Out (2008)

0.6

-3

10

0.4

-4

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0.2

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(a) 2005

-6

10

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11

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12

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-3

10

-2

-1

10
10
Cumulative heavy hitters

0

10

10

In
Out

Cumulative distribution-1

10

Figure 8: Cumulative distribution of traﬃc volume of heavy-hitters in decreasing order of volume: 2005 and 2008

-2

10

-3

10

-4

10

The plot reveals a skewed traﬃc distribution amongusers; the top N% of heavy-hitters use X% of the total traﬃc. For example, the top 4% use 75% of thetotal inbound traﬃc, and 60% of the outbound. The
distribution has not changed much from 2005 to 2008;
a small group of heavy-hitters similarly represent a signiﬁcant part of the total traﬃc. The overall traﬃc isstill dictated by the heavy-hitter traﬃc even with theincreased traﬃc by the client-type users, probably because the client-type users also have a long-tailed distribution.

-510(b) 2008

-6

10

4

10

10

5

6

7

8

9

10

10
10
10
10
10
Daily traffic per user (bytes)

11

10

10

12

Figure 7: Cumulative distribution of daily traﬃc
per user: 2005 (top) and 2008 (bottom).

Figure 8 shows the cumulative distribution of traﬃcvolume consumed by top ranking heavy-hitters, computed independently for inbound and outbound traﬃc.

4.4

Inbound and Outbound Correlation

To observe the ratio of inbound and outbound vol7

11

10

10

9
8

10

7

10

6

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(c) Fiber (2008)
Daily inbound traffic (byte)

Daily inbound traffic (byte)

10

10

10 4
10

(b) DSL (2005)

10

10

Daily inbound traffic (byte)

Daily inbound traffic (byte)

10

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(a) Fiber (2005)

5

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11

Figure 9: Correlation of inbound and outbound traﬃc volumes per user in one metropolitan prefecture: ﬁber (left) and DSL (right) in 2005 (top) and 2008 (bottom)

umes of each user, daily inbound and outbound volumes
per user are shown in log-log scatter plots in Figure
9. These are taken from a metropolitan prefecture but
the characteristics are common to all the prefectures.
The number of plotted users is about 4300 for ﬁber andabout 5400 for DSL in 2005 and about 14700 for ﬁber
and about 3400 for DSL in 2008; a clear shift from DSL
to ﬁber in 2008.
In all the plots, the highest density cluster is below
and parallel to the unity line where outbound volume(download for users) is about ten times larger than thatof inbound, which corresponds to the client-type users.
In a higher volume region, a diﬀerent cluster appears
to exist around the unity line, which corresponds to thepeer-type users.
The diﬀerence between the ﬁber and DSL plots isbasically heavy-hitters’ population share. It is also observed that there are heavy-hitters in the client-typegroup whose outbound volumes are comparable to thatof heavy-hitters in the peer-type group. It is especiallyevident in the plot of the ﬁber users in 2008.
However, the boundary between the client-type users
and the peer-type users as well as the boundary between the heavy-hitters and the normal users are notvery clear. It can be also observed that, across the entire traﬃc volume range, the inbound/outbound ratio
varies greatly, up to 4 orders of magnitude in all theplots. It suggests that there exit diverse users with adiﬀerent traﬃc mix of client-type and peer-type applications. That is, although it is possible to classify users
into client-type and peer-type at the macro level, actual
individual users have mixed traﬃc of both types.

4.5

Protocol and Port Usage

Table 4 shows the ranking of protocols and ports forthe total users, the client-type users, and the peer-typeusers in 2005 and 2008. The ranking is similar for all
the groups with minor diﬀerences in dynamic ports sothat the table is ordered by the ranking of the total
users in 2008.
To rank port numbers in TCP and UDP, we take thesmaller of the source and destination ports of a ﬂow.
TCP ports are further divided into well-known portsthat are smaller than 1024, and dynamic ports that are
equal to or larger than 1024. We do not distinguish registered ports from dynamic ports since many implementations use the registered port range from 1024 through49151 for dynamic ports. We believe that majority ofdynamic port traﬃc represent peer-to-peer applications
so that dynamic port traﬃc can be used as a roughestimate of peer-to-peer traﬃc.
When the total traﬃc in 2005 is compared with thatin 2008, port 80 (http) accounted only for 9% in 2005,
and increased to 14% in 2008. TCP dynamic ports accounted for 83% in 2005 and decreased to 78% in 2008.
That is, 5% shifted from dynamic ports to port 80. Still,8

content. Many of video content are also transported
over port 80 so that considerable video volume is supposed to be included in port 80. It is almost impossible to quantify the exact video volume in port 80 but,given the heavy-tailed size distribution of video content
which is much larger than traditional web content[10],it is likely that large part of the traﬃc on port 80 isalready video and other rich media content.
Figure 10 compares temporal behaviors of three port
groups: port 80, well-known ports other than port 80,
and dynamic ports for the total users, the client-typeusers and the peer-type users in 2005 and 2008. The volume is normalized to the peak value of the total traﬃcsize not to reveal the absolute traﬃc volume of the ISP.
The total traﬃc is heavily aﬀected by the peer-type
user group as expected. For the client-type users, it isclear that port 80 has increased and dynamic ports havedecreased. Port 554 has a temporal pattern similar to
port 80 but not shown in the plots.
For the peer-type users, most traﬃc is in dynamic
ports, and an increase in port 80 is also observed. The
daily ﬂuctuation is speculated to be caused by stopping ﬁle-sharing applications after ﬁles are downloaded
rather than leaving them running all the time. Thiscan be observed from the traﬃc pattern of the dynamic
ports that gradually decreases after midnight towards
morning with the bottom at around 8am. It is in contrast to the port 80 traﬃc that drops quickly after midnight with the bottom at around 4am. The diﬀerencein traﬃc patterns suggests that many ﬁle-sharing users
stop the application when they wake up in the morning.
This limited usage pattern of peer-to-peer ﬁle-sharing
is partly due to increasing number of ISPs imposing
limits on bandwidth usage, and partly due to users’risk awareness about leaving ﬁle-sharing applications as
many incidents caused by compromised ﬁle-sharing applications have been reported in Japan.
For ordinary users, peer-to-peer ﬁle-sharing becomesless attractive with the advent of rich content services
and their easy-to-use applications. At the same time,
peer-to-peer traﬃc will not go away anytime soon. Also,
the peer-to-peer mechanism itself could evolve into a
powerful engine to drive content disribution in large
scale, if it becomes more friendly to users and ISPs[30].

Table 4: Protocol breakdown: 2005 and 2008
2005
2008
total client peer total client peer(%) type type(%) type type
TCP*
97.43 94.93 97.66 96.00 95.51 96.06(< 1024)
13.99 58.93 8.66 17.98 76.16 11.35
80 (http)
9.32 50.78 5.54 14.06 64.96 8.26
554 (rtsp)
0.38 2.44 0.19 1.36 8.21 0.58
443 (https)
0.30 1.45 0.19 0.58 1.63 0.46
20 (ftp-data)
0.93 1.25 0.90 0.24 0.17 0.25
81 (-)
0.15 0.04 0.16 0.23 0.04 0.25
82 (-)
0.05 0.01 0.06 0.19 0.01 0.21
110 (pop3)
0.17 1.00 0.10 0.14 0.46 0.10
22 (ssh)0.09 0.17 0.10 0.10 0.16 0.09
25 (smtp)
0.14 0.51 0.11 0.07 0.11 0.07
1000 (-)
0.03 0.02 0.04 0.07 0.01 0.08
others2.52 1.26 1.27 0.94 0.40 1.00(>= 1024)
83.44 36.00 89.00 78.02 19.35 84.71
6346 (gnutella)
0.92 0.84 0.93 0.94 0.67 0.97
6699 (winmx)
1.40 1.14 1.43 0.68 0.24 0.737743 (winny)
0.48 0.15 0.51 0.30 0.04 0.33
1935 (rtmp)
0.20 0.81 0.14 0.22 0.73 0.16
6881 (bittorrent) 0.25 0.06 0.27 0.22 0.02 0.247144 (-)- 0.02 0.03 0.19 0.03 0.218080 (-)
0.11 0.15 0.14 0.11 0.09 0.154662 (edonkey)
0.12 0.02 0.13 0.13 0.00 0.14
0.03 0.02 0.10 0.04 0.01 0.1111560 (-)
3074 (-)
0.02 0.03 0.10 0.02 0.32 0.07
others
79.91 32.76 85.22 75.17 17.20 81.60
UDP*
1.38 3.41 1.19 1.94 2.50 1.88
53 (dns)0.03 0.14 0.02 0.04 0.12 0.03
others1.35 3.27 1.17 1.90 2.38 1.85
ESP
1.09 1.35 1.06 1.93 1.85 1.94
0.07 0.12 0.06 0.09 0.08 0.09
GRE
ICMP
0.01 0.05 0.01 0.02 0.05 0.02
0.02 0.14 0.02 0.02 0.01 0.01
othersprotocolport

78% of the total traﬃc in 2008 are between dynamic
ports and most of them are considered to be peer-topeer traﬃc. The usage of each dynamic port is small
because recent peer-to-peer applications use arbitraryports. The largest one, port 6699 in 2005 is only 1.4%
and port 6346 in 2008 is only 0.9%. It is evident that
it is no longer possible to make use of port numbers for
identifying applications.
To observe diﬀerences in peer-type and client-typeusers, the users are classiﬁed simply by average daily inbound volume with the threshold of 100MB/day. This
threshold is roughly at the center of the two distributions in the inbound probability density functions inFigure 6. In our previous work, we used 2.5GB/day to
distinguish heavy-hitters to focus on heavy-hitters but
100MB/day is used here to focus on client-type users.
For the client-type users, port 80 accounted for 51%
in 2005 and increased to 65%. Dynamic ports accounted
for 36% in 2005 and decreased to 19% in 2008. Port
80 is much larger in this group, and 17% shifted fromdynamic ports to port 80 and other well-known ports.
Also, there is a noticeable increase in port 554 used for
the Real Time Streaming Protocol (RTSP), from 2% to
8%. RSTP is a control protocol for streaming content
employed by major streaming services. RTSP has theinterleaved mode where the video data stream is interleaved on the original TCP control connection. Thus,
the increase in port 554 is an evidence of increased video

4.6

Summary

The overall traﬃc is still dominated by heavy-hitters,
mainly using peer-to-peer applications. However, theirtraﬃc decreased slightly in both population share andvolume share.
The current slow growth rate is attributed to the factthat the dominant aggressive peer-to-peer traﬃc is not
growing much. On the other hand, the client-type trafﬁc is slowly moving towards high volume usage. The
circumstantial evidence indicates that it is driven by
9

Figure 10: Temporal behavior of port usage: total users (top), client-type users (middle) and peertype users (bottom) in 2005 (left) and 2008 (right)

video and other rich media content. The increase ofvideo content is, however, not yet very visible in the
total residential traﬃc volume at the macro level. We
will examine the growth of this client-type user traﬃcin the next section.
Meanwhile, the capacity of the Internet, both accessnetworks and core networks, will continue to grow. The
annual growth of the access capacity is reported andprojected to be 50% per year by the FTTH Council
Europe[7] and, in fact, Japanese residential users are
shifting from DSL to ﬁber access. The backbone networks also have been increasing capacity fast enough tohandle the demands. TeleGeography[28] reports thatannual international Internet capacity growth was 45%
in 2004-2006 and 68% in 2007.
If these trends continue, the traﬃc growth will stayat a modest rate comparable to the growth of network
capacity. Video content is steadily growing but unlikely
to disastrously overﬂow the global Internet in the shortterm.

lognormally distributed. Because the distribution has along tail, its simple arithmetic mean is heavily aﬀectedby the tail of the distribution. The mean is of interest
as it is directly relates to the total traﬃc volume. We
examine the traﬃc characteristics using the propertiesof the lognormal distribution.
It is not surprising that per-customer traﬃc volume
follows a lognormal distribution because a multiplicative stochastic process leads to a lognormal distribution [9], and traﬃc growth is well modeled by a multiplicative stochastic process; each user increases traﬃcfollowing a growth rate which is an independent identically distributed random process. Similar observations
were reported in the distribution of traﬃc volume at anorganization level[8] as well as in a growth model of web
pages[13].
The probability density function of a log-normal distribution is given by1√

5. DISCUSSION
Video and rich media content in residential traﬃc will
have a signiﬁcant impact on the traﬃc growth in the
future[5]. Hence, we focus on the client-type users andanalyze their ourbound traﬃc as this group is characterized by download volume.
From the probability density function in Figure 6,
traﬃc volume per user for the client-type group is roughly

exp(−(ln x − µ)2
)
2σ 2

(1)
xσ 2π
where µ and σ are the mean and standard deviation ofln(x). The mean of the original distribution, p(x), isp(x) =

E(x) = exp(µ + σ 2 /2)(2)

and the variance of p(x) is exp(2µ + σ 2 )(exp(σ 2 ) − 1).
By ﬁtting a lognormal distribution to the outbound of
the total traﬃc in 2005 and 2008 with nonlinear leastsquare Marquardt-Levenberg algorithm, we estimatedµ = 17.29, σ = 1.813 for 2005 and µ = 18.36, σ =10

2.105 for 2008. Thus, the mode and mean of p(x) arecomputed from µ and σ as 32.2MB and 166.7MB for
2005, and 93.8MB and 860.0MB for 2008. The modeof 93.8MB/day seems reasonable considering popular
web sites with rich media content (e.g., the mean ﬁlesize of YouTube videos is 10MB[10], and popular applications such as Google Maps proactively fetch images
in background). The mean of 860MB/day, however,seems much larger than one would expect but its annual growth rate is still within a reasonable range.
The compound annual growth rate (CAGR) is 1.39
for the mode and 1.65 for the mean, computed by 3.28
years that is between February 21-27 2005 and June 2-8
2008. The growth rate of the mean is much larger thanthat of the mode as characterized by Equation 2 where
the mean grows much faster than the mode. Also, σslightly increased from 2005 to 2008, which means larger
contributions from the tail of the distribution.
Table 5 shows simplistic growth projections using both
additive and multiplicative growth. Traﬃc growth has
been proved to be multiplicative but additive growthis shown as the lower bounds. The simple arithmetic
mean of the original data is also listed in the table astotal mean. With multiplicative growth, the mode andthe median will be 252MB/day and 3.9GB/day in 2011.

As the Internet becomes a social communication platform, individual users of all ages start actively participating in digital communications with diverse content,
notably with much richer images and videos than traditional web content. The traﬃc mix and volume arechanging accordingly.
It is diﬃcult to predict future traﬃc. As we havewitnessed in the past, the advent of new applications
would change the traﬃc mix and, accordingly, growthrate. However, at the macro level, traﬃc will keep growing.
There will be many challenges posed by unprecedented
traﬃc volume. A variety of technical solutions exist
to mitigate possible problems such as content caching,content distribution networks and preferential qualityof service. It also involves with economic factors suchas the access service cost and the costs of backbone
capacity and equipment as well as political and social
factors such as net-neutrality and content management.
Technical and solid observations on traﬃc are essential
to understand changes and adapt to new demands.
In this paper, we have shown that the apparent slow
traﬃc growth is due primarily to the stalled growth ofpeer-to-peer traﬃc that is still dominant in the current
traﬃc. At the same time, the usage of ordinary users is
slowly swelling with increased diversity, driven by video
and other rich media content. At the macro level, theimpact of video content is still small compared withpeer-to-peer traﬃc, and continue to be so in the next
few years.
As for the generality of our results, some aspects are
speciﬁc to Japanese traﬃc such as the high penetration of ﬁber access, geographic concentration and language barriers. Nevertheless, our key ﬁndings seem tobe common to other countries, although the exact ratioof traﬃc mix and their growth should be diﬀerent fromcountry to country, and probably, from ISP to ISP.
Persistent and consistent data collection is essential
for this type of study. We will continue monitoring trafﬁc by collecting aggregated traﬃc logs from the participating ISPs, and by analyzing per-customer traﬃc,
hopefully from more ISPs.

Table 5: Simplistic growth projections for outbound traﬃc per user (MB/day)additive growth multiplicative growth
total lognormal total
lognormal
mean mode mean mean modemean
2005 Feb 446.6 32.2 166.7 446.6 32.2
166.7
2008 Jun 796.6 93.8 860.0 796.6 93.8
860.0
growth/yr 106.7 18.8 211.4 1.19 1.39
1.65
2009 Jun903
113 1071
948
130
1419
2010 Jun
1010
131 1281 1128
181
2341
2011 Jun
1117
150 1494 1342
252
3863

Since the distribution is long-tailed, the mean is dictated by a small segment of users at the tail and becomes less predictable than the mode. We do not thinkthat the mean keeps growing with this rate because
2005 is before popular video services took oﬀ so that
this index could be overestimated. Also, a larger bias
against heavy-hitters will take place as traﬃc volume
approaches to the access link capacity. However, atleast, there is a potential that traﬃc of ordinary users
could grow to a substantial volume.
The growth looks signiﬁcant but it is similar to how
the web traﬃc growth looked in late 1990 when people
were still using dial-up access. From an optimistic view,
technical advances in access and core networks are likely
to oﬀset the traﬃc growth of this level.

Acknowledgments
We are grateful to the following ISPs for their support
and assistance with our data collection; IIJ, SoftBank
Telecom, K-Opticom, KDDI, NTT Communications,and SoftBank BB. We would like to thank the Ministry of Internal Aﬀairs and Communications of Japanfor their support in coordinating our study.
K.F. is partially supported by the Grant-in-Aid forYoung Scientists (B) from MEXT Japan.

7.
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