The Impact of Residential Broadband Traffic
on Japanese ISP Backbones
Kensuke Fukuda

Kenjiro Cho

Hiroshi Esaki

NTT/WIDE

IIJ/WIDE

U.Tokyo/WIDE

fukuda@t.ecl.net

kjc@iijlab.net

hiroshi@wide.ad.jp

Number of subscribers [million]

ABSTRACTThis paper investigates the effects of the rapidly-growing
residential broadband traffic on commercial ISP backbone
networks. We collected month-long aggregated traffic logs
for different traffic groups from seven major ISPs in Japan inorder to analyze the macro-level impact of residential broadband traffic. These traffic groups are carefully selected to besummable, and not to count the same traffic multiple times.
Our results show that (1) the aggregated residential broadband customer traffic in our data exceeds 100Gbps on average. Our data is considered to cover 41% of the totalcustomer traffic in Japan, thus we can estimate that the total residential broadband traffic in Japan is currently about
250Gbps in total. (2) About 70% of the residential broadband traffic is constant all the time. The rest of the traffic
has a daily fluctuation pattern with the peak in the evening
hours. The behavior of residential broadband traffic deviates considerably from academic or office traffic. (3) The
total traffic volume of the residential users is much higher
than that of office users, so backbone traffic is dominatedby the behavior of the residential user traffic. (4) The traffic volume exchanged through domestic private peering iscomparable with the volume exchanged through the major
IXes. (5) Within external traffic of ISPs, international traffic
is about 23% for inbound and about 17% for outbound. (6)The distribution of the regional broadband traffic is roughly
proportional to the regional population.
We expect other countries will experience similar traffic
patterns as residential broadband access becomes widespread.

1.

DSL
CATV
FTTH

10
8
6
4
2
0

2000/1 2001/1 2002/1 2003/1
Year

2004/1Figure 1: Increase of residential broadband subscribers in Japanand 1 million FTTH subscribers [16]. The number of broadband access subscribers is still increasing as shown in Figure
1 [16]. At the same time, broadband access technologies are
shifting to higher speed such as 50Mbps DSL and 100Mbps
FTTH.
Aggregated IX traffic [Gbps]

100

INTRODUCTION

80
60
40
20
0

The availability of residential broadband access has madetremendous advances over the past few years, especially in
Korea and Japan where both the penetration rate and theaverage line speed are much higher than other countries. A
government survey shows that there are 14.5 million broadband subscribers in Japan as of February 2004; 11 million
DSL subscribers, 2.5 million CATV Internet subscribers,

ACM SIGCOMM Computer Communications Review

122000/1 2001/1 2002/1 2003/1 2004/1
Year

Figure 2: Traffic growth at the major Japanese IXes

As residential broadband access becomes widespread, weare observing an unprecedented traffic increase on commercial backbone networks. Figure 2 shows the aggregated peaktraffic at major IXes (JPNAP[8], JPIX[7], and NSPIXP[10])in Japan, and illustrates the growth in backbone traffic [16].
The impact of residential broadband traffic is not only in volume but also in usage patterns. The peak hours have shiftedfrom office hours to evening hours, and emerging file sharing or other peer-to-peer communications with audio/video
contents exhibit behavior considerably different from traditional world wide web [14, 6]. There are striking differencesin traffic patterns from earlier observations [13, 4, 9, 3, 5].

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Volume 35, Number 1: January 2005

Although a drastic change in backbone traffic has alreadybeen observed, it is difficult to plan for the future because
residential broadband traffic is undergoing a transformation;
new innovations in access networking technologies continue
to be developed, and new applications as well as their usage are emerging to take advantage of low-cost high-speed
connectivity.
There is a strong concern that, if this trend continues,
Internet backbone technologies will not be able to keep up
with the rapidly growing residential traffic. Moreover, commercial ISPs will not be able to invest in backbone networks
simply for low-profit residential traffic.
It is critical to ISPs and policy makers to understand theeffects of growing residential broadband traffic but it is difficult both technically and politically to obtain traffic datafrom commercial ISPs. Most ISPs are collecting traffic information for their internal use but such data contain sensitive
information and are seldom made available to others. In addition, measurement methods and policies differ from ISP to
ISP so that it is in general not possible to compare a data
set with another set obtained from a different ISP.
In order to seek out a practical way to investigate theimpact of residential broadband traffic on commercial backbone networks, we have formed an unofficial study groupwith specialists including members from seven major commercial ISPs in Japan.
Our goal is to identify the macro-level impact of residential broadband traffic on ISP backbones. More specifically,
we are trying to obtain a clearer grasp of the ratio of residential broadband traffic to other traffic, changes in traffic
patterns, and regional differences across different ISPs. As
the first step, we have collected aggregated bandwidth usage
logs for different traffic groups. Such statistics will provide
reference points for further detailed analysis, most likely bysampling methods. In this paper, we report findings in ourdata sets that residential broadband traffic presents a significant impact on ISP backbones.

2.

(B1)
external 6IXes
JPNAP/JPIX/NSPIXP(B3)external international

external edge

ISP
customer edge

(A1)
RBB customers
DSL/CATV/FTTH(C) prefectural(A2)
non-RBB customers
leased lines
data centersdialupFigure 3: 5 traffic groups at ISP boundary for datacollection

Internet so that these groups are chosen by the existing operational practice of the participating ISPs. We re-aggregate
each ISP’s aggregated logs, and only the resulting aggregated traffic is used in our study so as to not reveal a shareof each ISP.
Our main focus is on (A1) RBB (Residential Broadband) customers but other items are used to understandthe relative volume of (A1) with respect to other types oftraffic as well as to cross-check the correctness of the results.
(A2) non-RBB customers is used to obtain the ratio of
residential broadband traffic to total customer traffic. The
total customer traffic (A) is (A) = (A1) + (A2). (B1) external 6IXes and (B2) external domestic are used toestimate the coverage of the collected data sets. (B3) external international is used to compare domestic trafficwith international traffic. The total external traffic (B) is(B) = (B1) + (B2) + (B3). (C) prefectural is to observe regional differences. This group covers only 2 major
residential broadband carriers who provide aggregated links
per prefecture to ISPs; other carriers’ links are not based on
prefectures. This group is a subset of (A1).
In general, it is meaningless to simply sum up traffic values from multiple ISPs since a packet could cross ISP boundaries multiple times. Customer traffic is, however, summablebecause a packet crosses customer edges only once in eachdirection, when entering the source ISP and exiting the destination ISP. The numbers for external traffic are overestimated since a packet could be counted multiple times if it
travels across more than 2 ISPs. However, the error should
be relatively small in this particular result since these ISPs
are peering with each other.
We collected month-long traffic data that was sampled every two hours from the participating ISPs because a 2-hourresolution is the highest common factor for month-long data.
This is because both MRTG and RRDtool aggregate oldrecords into coarser records in order to bound the databasesize. In MRTG, 2-hour resolution records are maintained for
31 days in order to draw monthly graphs. RRDtool does nothave fixed aggregation intervals but most operators configure RRDtool to maintain 1-hour or 2-hour resolution records
for a period longer than needed for monthly graphs.
We developed a perl script to read a list of MRTG andMETHODOLOGY

There are several requirements in order to solicit ISPs toprovide traffic information. We need to find a common data
set which all the participating ISPs are able to provide. The
required workload and investment for ISPs to provide thedata set should not be high. The data set should be coarse
enough not to reveal sensitive information about the ISP
but be meaningful enough so that the behavior of residential
broadband traffic can be analyzed. It is also desirable to beable to cross-check the consistency of the results with otherdata sets. The data sets should be summable in order toaggregate them with those provided by other ISPs.
We found that most ISPs collect interface counter valuesof almost all routers in their service networks via SNMP,
and archive per-interface traffic logs using MRTG [12] or
RRDtool [11]. 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 canbe roughly divided into customer traffic, and external traffic such as peering and transit. For practical purposes, weselected the 5 traffic groups shown in Figure 3 for data collection. The descriptions of the groups are in Table 1. It
is impossible to draw a strict line for grouping (e.g. residential/business and domestic/international) on the global

ACM SIGCOMM Computer Communications Review(B2)external domestic
local IXes
private peering/transit

16

Volume 35, Number 1: January 2005

Table 1: Descriptions of traffic groups
traffic group(A1) RBB customers

description
residential broadband customer lines(A2) non-RBB customers

includes leased lines, data centers, dialup lines(B1) external 6IXes

links for 6 major IXes (JPNAP/JPIX/NSPIXP
in Tokyo/Osaka)
external domestic links other than the 6IXes (regional IXes, private peering, transit)(B2) external domestic(B3) external international(C) prefecturalexternal international links
RBB links divided into 47 prefectures in Japan

RRDtool log files, and aggregate traffic measurements for agive period with a given resolution. It outputs “timestamp,
in-rate, out-rate” for each time step. Another script produces a graph using RRDtool. We provided the tools to the
ISPs so that each ISP can create aggregated logs by themselves. It allows ISPs not to disclose the internal structureof their network or unneeded details of its traffic.
The biggest workload for the ISPs is to classify the large
number of per-interface traffic logs and create a log list foreach group. For large ISPs, the total number of the existing per-interface traffic logs exceeds 100,000. To reduce theworkload, ISPs are allowed to use the internal interface of aborder router instead of a set of external (edge) interfaces
if the traffic on the internal interface is an approximation of
the sum of the external interfaces. In this case, we instruct
the tool to swap “in” and “out” records since the notation in
the per-interface logs depicts the perspective of the routers
but inbound/outbound records in our data sets signify the
ISPs’ point of view.

3.

prefectural links from 2 RBB carriers

independent of daily user activities. The diurnal patternindicates that home user traffic is dominant, i.e., the traffic increases in the evening, and the peak hours are from21:00 to 23:00. Weekends can be identified by larger daytime traffic although the peak rates are close to weekdays.
The outbound traffic to customers is slightly larger than theinbound, even though it is often assumed that home users’
downstream traffic is much larger than upstream. We believe that peer-to-peer applications contribute significantly
to the upstream traffic.

RESULTS

Figure 5: Aggregated non-RBB customer weekly
traffic in September 2004
Figure 5 shows the weekly traffic of non-RBB customers(A2). This group contains leased lines, data centers, andother customers (e.g., dialup customers). It also includes
leased lines used to accommodate residential broadband access within the customer networks (e.g., second or third level
ISPs) since ISPs do not distinguish them from other leasedlines. As a result, the traffic pattern still appears to be
dominated by residential traffic, which is indicated by thepeak hours and the differences between weekdays and weekends. However, we also observe office hour traffic (from 8:00
to 18:00) in the daytime on weekdays but traditional office commercial traffic appears to be smaller than residential

Customer Traffic

Figure 4 shows the weekly traffic of RBB customers, consisting of DSL/FTTH/CATV residential users (A1). This
group also includes small business customers using residential broadband access. Note that the plot is the mean rate
and not the peak rate, even though the peak rate is often used for operational purposes. The residential broadband customer traffic has already exceeded 100Gbps in total. The inbound and outbound traffic are almost equal,
and about 70Gbps is constant for both directions, probably due to peer-to-peer applications which generate traffic

ACM SIGCOMM Computer Communications Review

domestic: both link-ends in Japan.
includes domestic peering with global
ASes

Figure 4: Aggregated RBB customer weekly traffic in September 2004. Darker vertical dotted lines
indicate the start of the day (0:00 am in local-time).

We analyzed traffic logs for September and October in2004 from seven major ISPs in Japan. Each ISP provided
traffic logs with 2-hour resolution for those two months. The
results were obtained by aggregating all the traffic logs provided by the seven ISPs. 2-hour boundaries were computedin UTC by MRTG and RRDtool so that they fell on odd
hours in Japanese Standard Time that is nine hours aheadof UTC.
For weekly data analysis, we took the averages of thesame weekdays in the month. We excluded two holidays
in September and one holiday in October from the weekly
analysis since their traffic pattern is closer to that of weekends. We also excluded another two days in October fromthe weekly analysis as one ISP failed to record traffic logs
during this period.

3.1

notes
includes small business customers using RBB
may include RBB customers behindleased lines

17

Volume 35, Number 1: January 2005

customer traffic. Note that we cannot directly compare thetraffic volume of (A2) with that of (A1) because (A2) was
provided by only four of the seven ISPs.

Figure 9: Weekly international external traffic in
September 2004

Figure 6: Aggregated total traffic from ABILENE
in October 2004. Time is in CDT.

the traditional behavior of content downloading to Japan
still dominates international traffic.

3.3

The traffic patterns common to Figure 4 and 5 are quite
different from well-known academic or business usage patterns. For example, Figure 6 shows the weekly traffic of
ABILENE [1], an Internet2 backbone network for universities and research labs. From Figure 6, it is clear that officehour traffic is dominant; traffic peaks occur around noon,
and there is less user activity on weekends.

3.2

Prefectural Traffic

In order to investigate regional differences (i.e., between
metropolitan and rural areas), we collected regional traffic rates of the 47 prefectures. Figure 10 illustrates aggre-

External Traffic

Figure 7: Weekly external traffic to/from the 6 major IXes in September 2004
Figure 10: Example prefectural traffic: a metropolitan prefecture (top) and rural prefecture (bottom)
The external traffic groups are used to understand thetotal traffic volume in Japan. Figure 7 shows traffic to andfrom the six major IXes (B1). It is apparent that the traffic
behavior is strongly affected by residential traffic.

gated traffic of one metropolitan prefecture (top graph) andof one rural prefecture (bottom graph). Both graphs exhibit similar temporal patterns such as peak positions andweekday/weekend behavior. In addition, about 70% of theaverage traffic is constant regardless of the traffic volume.
These characteristics are common to other prefectures. One
noticeable difference found is that metropolitan prefectures
experience larger volumes of office hour traffic, probably due
to heavy business usage.
Figure 11 is a scatter plot of traffic and population for
the 47 prefectures. We found that a prefecture’s traffic isroughly proportional to the population of the prefecture.
We obtained similar results when the number of Internet
users found in [15] is used instead of the population. The
result indicates that there is no clear regional concentrationof heavy hitters of the Internet. That is, the probability offinding a heavy hitter in a given population is constant.
In order to analyze the scaling property of traffic volume— to find a typical size of prefectural traffic volume, we showthe (complementary) cumulative distribution of prefectural
traffic on a log-log scale in Figure 12. The plot conforms
to a power law distribution with a cutoff point at 700Mbps,
meaning that there is no typical size of prefectural traffic
volume. In other words, most prefectures generate a small

Figure 8: Weekly other domestic external traffic in
September 2004
Figure 8 shows the external domestic traffic (B2) including
regional IXes, private peering and transit but not including
traffic for the six major IXes. The traffic pattern is very
similar to Figure 7.
Figure 9 shows international traffic (B3). The inboundtraffic is much larger than the outbound, and the traffic
pattern is clearly different from the domestic traffic. The
peak hours are still in the evening, but outbound traffic
volume fluctuates less than inbound traffic, suggesting that

ACM SIGCOMM Computer Communications Review

18

Volume 35, Number 1: January 2005

Table 2: Average rates of aggregated customer traffic(A1)customer-RBB (A2)customer-non-RBB(7 ISPs)(4 ISPs)
inbound outbound inboundoutboundSep
98.1G
111.8G
14.0G
13.6G
Oct 108.3G
124.9G
15.0G
14.9G
Table 3: Average rates of aggregated external traffic

Sep
Oct

Figure 11: Relationship between population andtraffic for prefectures(B3)ext-intl(7 ISPs)
inout
25.3G 14.1G
27.7G 15.4G

Table 4: Average rates of total customer traffic andtotal external traffic(A)customer(A1+A2) (B)external(B1+B2+B3)
inboundoutboundinboundoutboundSep
112.1G
125.4G
109.4G
82.8G
Oct 123.3G
139.8G
117.1G
88.8G

010
Cumulative distribution(B2)ext-dom(7 ISPs)
inout
48.2G 37.8G
53.1G 41.6G

Table 3 summarizes the average rates of aggregated external traffic. We observe that the total volume of external
domestic traffic (B2), mainly private peering, exceeds thevolume for the six major IXes (B1). From this result, it
can be concluded that simply relying on data from IXes toestimate and understand nation-wide traffic may be misleading, because a considerable amount of traffic is exchanged
by private peering. At the same time, it is possible thatthe volume of private peering is larger in our measurement
than the rest of the Japanese ISPs because private peering
is usually exercised only between large ISPs. The ratio ofinternational traffic to the total external traffic is 23% forinbound and 17% for outbound.

amount of traffic, still prefectures with high traffic volume
are observable with a certain probability. It is also observedthat the plots for the top 5 largest prefectures deviate fromthe power law. To investigate this power law decay, weshow the cumulative distribution of prefectural populationsin the sub-panel. The plots reveal that the power law appearing in traffic volume is derived from the power law decayof prefectural populations, as can be inferred from the linear relationship between traffic and populations in Figure
11. Thus, we can conclude that the probability of finding aheavy hitter in a given population is constant and the distribution of aggregated traffic volume directly depends on
the population.

in
out

0.8
0

-1

10

10

There is a relationship between the total customer traffic(A1 + A2) and the total external traffic (B1 + B2 + B3)
in Table 4. If we assume all inbound traffic from other ISPs
is destined to customers, the inbound traffic volume for the
total external traffic (B) should be close to the outboundtraffic volume for the total customer traffic (A). Similarly,
the outbound traffic volume of (B) should be close to theinbound traffic volume of (A). However, the non-RBB customer data is provided by only 4 ISPs. If we interpolate themissing ISPs in the non-RBB customer traffic using the ratio from the four reporting ISPs, the total inbound customer
traffic is estimated to be 152.1Gbps, and that outbound to
be 167.8Gbps. Though these volumes are higher than thosefor the total external traffic, this is probably because thetotal customer traffic contains traffic whose source and destination belong to the same ISP.
Lastly, we examined the relationship between our IX traffic data (B1) and the total input rate of the six major IXes,
as obtained directly from these IXes [16]. In comparison
with the published total incoming traffic of these IXes, ourdata represent 41% of the total traffic as shown in Table 5.
If we assume this ratio to be the traffic share of the seven
ISPs, the total amount of residential broadband traffic in
Japan is roughly estimated to be 250Gbps.
To check consistency, we collected the September results

-1

10

-2

10

10

1000
10000
Population [x 1000]

-2

100

1000
Traffic [Mbps]

Figure 12: Cumulative distribution of prefectural
traffic. Sub-panel indicates the cumulative distribution of populations for comparison.

3.4(B1)ext-6ix(7 ISPs)
inout
35.9G 30.9G
36.3G 31.8G

Summary of Traffic

The monthly average rates in bits/second of the traffic
groups are shown in Tables 2 through 5.
Table 2 is the average rates of aggregated customer traffic. As explained before, the non-RBB customer traffic was
obtained only from the four ISPs so that it is difficult to
directly compare (A1) with (A2). Thus, we estimated theratio of the RBB customer traffic (A1) to the total customer traffic (A) from only four ISPs’ data with both (A1)and (A2). The estimated ratio (A1)/(A1+A2) is 65% forinbound and 67% for outbound.

ACM SIGCOMM Computer Communications Review

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Volume 35, Number 1: January 2005

0

0

10

10
10

-2

10
10

-3

-4
-5

in
out

10

-6

10

10
10

10

-1

Cumulative distribution-1

Cumulative distribution

Cumulative distribution

10

-2
-3

10
10

-4

10

-5

in
out

-6

4

10

5

10

6

7

8

9

10

10
10
10
10
10
Daily traffic per user [bytes]

10

11

10

10

4

10

5

6

7

8

9

10

10
10
10
10
10
Daily traffic per user [bytes]

10

10
10

-3

-4
-5

in
out

10
10

11

-1

-2

10
10

0

-6

10

4

10

7

6

5

8

9

10

11

10
10
10
10
10
Daily traffic per user [bytes]

10

Figure 13: Cumulative distribution of daily traffic per user: all prefectures (left), a metropolitan prefecture(middle) and a rural prefecture (right)

sistent across different prefectures, and the differences are
only in the tail length affected by the number of customers,
which confirms that the distribution of heavy hitters is similar across different regions.

Table 5: IX traffic observed from ISPs and from
IXes
(B1)ext-6ix traffic observed by IXes
outboundinbound
Sep
30.9G
74.5G
Oct
31.8G
77.1G

Daily inbound traffic [bytes]

10

and the October results separately in October and November respectively. These results are consistent so that we are
fairly confident about their accuracy. However, the traffic increase from September to October was higher than our
projection; the traffic of the six IXes increased by only about
3% but the other groups increased by about 10%. We suspect that some links missing in the September measurements
could have been added later for the October measurements,
and we expect further measurement will shed light on thisissue.

10

9

10

8

10

7

10

6

10

5

10
10

4
3

10 3
10

10

4

5

6

7

8

9

10
10
10 10
10
10
Daily outbound traffic [bytes]

10

11

10

Figure 14: Correlation of inbound and outboundtraffic volumes in one metropolitan prefecture

3.5 Distribution of per-customer traffic
In order to verify our assumption that the distribution ofheavy hitters is similar across different regions, we obtained
per-customer traffic information for October 2004 from oneof the participating ISPs. The inbound/outbound traffic
volumes of residential broadband customers for each prefecture were collected by means of sampled NetFlow [2] andmatching customer IDs with the assigned IP addresses. Although this data set is from only one ISP, the results appear
to be consistent with the aggregated results. The results
are also consistent with earlier measurements on peer-topeer traffic by Sen and Wang [14]; peer-to-peer traffic is
extremely variable and highly skewed among participating
nodes.
Figure 13 shows the (complementary) cumulative distribution of daily traffic per customer on a log-log scale, andcompares all the prefectures (left) with one metropolitan
prefecture (middle) and one rural prefecture (right). The
daily traffic volume is the average of the month, and the
distribution is computed independently for inbound and outbound traffic. It is common to the three plots that about 4%of the customers use more than 2.5GB/day (or 230kbits/sec)
and, beyond this point, the slope of the distribution changes.
Thus, heavy hitters can be statistically identified as customers using more than 2.5GB/day. The distribution also
shows that outbound traffic is dominant for most customers
but it does not hold for heavy hitters. These trends are con-

ACM SIGCOMM Computer Communications Review

11

10

Figure 14 is a log-log scatter plot to show the correlation
between inbound and outbound traffic volumes for each customer. There is a positive correlation as expected, and thehighest density cluster is below and parallel to the unity
line where the volume of outbound (down-streaming for customers) is about ten times larger than that of inbound. Thisis probably due not only to application characteristics but
also to the restriction of asymmetric access lines. In a higher
volume region, a different cluster appears to exist aroundthe unity line. The slope of the cluster seems to be slightly
larger than 1, which explains the inversion of inbound andoutbound traffic volumes in Figure 13. A plausible interpretation of excess upstream traffic of heavy hitters is that symmetric high bandwidth of FTTH access lines complements
the shortage of upstream bandwidth of DSL lines. It canbe also observed that, across the entire traffic volume range,
the inbound/outbound traffic ratio varies greatly, up to 4 orders of a magnitude. This plot is taken from a metropolitan
prefecture but the correlation is common to all prefectures.
Figure 15 shows the cumulative distribution of traffic volume of all of the prefectures with heavy hitters in decreasing order of volume. Again, the distribution is computed
independently for inbound and outbound traffic. The graphreveals skewed traffic distribution among customers; the top20

Volume 35, Number 1: January 2005

1

Cumulative traffic

0.8

Acknowledgments
inout

We are grateful to the following ISPs for their support andassistance with our data collection; IIJ, Japan Telecom, KOpticom, KDDI, NTT Communications, POWEREDCOM,
and SOFTBANK BB. We would like to thank the Ministry of Internal Affairs and Communications of Japan for
their support in coordinating our study, and for providing
the statistics of broadband subscribers. We would also liketo thank Akira Kato of the University of Tokyo, Atsushi
Shionozaki of Sony CSL, Randy Bush of IIJ America, andanonymous reviewers for their valuable input.

0.6
0.4
0.2
0 -6
10

-5

10

-4

-3

-2

10
10
10
Cumulative heavy hitters

-1

10

0

10

5.
Figure 15: Cumulative distribution of traffic volume
with heavy hitters in decreasing order of volume

N% of heavy hitters use X% of the total traffic. For example,
the top 4% of customers at the knee point of the distribution
in Figure 13 use 75% of the total inbound traffic, and 60%of the outbound.

4.

CONCLUSION

The widespread deployment of residential broadband access has tremendous implications to our lives. Although its
effects to the Internet infrastructure are difficult to predict,
it is essential for ISPs to prepare for the future to accommodate innovations brought by empowered end-users.
Residential broadband traffic has already contributed toa significant increase in commercial backbone traffic. In ourstudy, residential broadband traffic accounts for two thirdsof the ISP backbone traffic, which should have a significant
impact on the pricing and cost structures of the ISP business.
The properties of residential broadband traffic differ considerably from those of academic or office traffic often seen inliterature. The constant portion of daily traffic fluctuations
is about 70%, much larger than ones found in earlier reports[3, 5]. Research results obtained from campus or other academic networks may no longer apply to commercial traffic.
More research efforts should be directed to measurement
and analysis of residential broadband traffic.
The inbound/outbound rates are roughly equal throughout our data sets. Many access technologies employ asymmetric line speed for inbound and outbound based on theassumption that content-downloading is dominant for normal users. However, this assumption does not hold in our
measurements.
Our measurements also suggest that a large amount oftraffic is exchanged by private peering so that data from
IXes may not be an appropriate index of nation-wide traffic
volume.
The prefectural results show that traffic volume is roughly
proportional to regional population. It indicates a uniquecharacteristic of the cyber-world in which activities are notbound by time and place. If this is the case, it would affectthe design of capacity planning for the future Internet.
For future work, we will continue collecting aggregatedtraffic logs from ISPs. We are also planning to do moredetailed analysis of residential broadband traffic by selecting
a few sampling points.

ACM SIGCOMM Computer Communications Review

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Volume 35, Number 1: January 2005