Towards Interdomain Transit Traffic Reduction in
Peer-assisted Content Delivery Networks
Hirochika Asai

Hiroshi Esaki

The University of Tokyo
panda@hongo.wide.ad.jp

The University of Tokyo
hiroshi@wide.ad.jp

Abstract—Transit traffic exchanged with transit providers
costs more for network providers compared to intra-domain
traffic or traffic exchanged over peering links. We have measured
and analyzed the peer distribution in BitTorrent, which is oneof peer-assisted content delivery networks (CDNs). From thepeer distribution, we show the potential of the high-cost transit
traffic reduction. We then propose a peer selection preference
which takes into account the economical relationships among
Autonomous Systems (ASes) in peer-assisted CDNs to reduce thehigh-cost transit traffic. Since most commercial Internet service
providers cannot disclose the relationships due to their commercial contract, we employ degree-based heuristics for inferring
the relationships; degree can be approximated from publicly
available BGP routing tables. We show that the peer selection
method utilizing the proposed preference can reduce interdomaintransit traffic exchanged with provider ASes by trace-driven
computer simulation. The significance of this paper are 1) weshow the potential of the transit traffic reduction from peer
distribution analysis, and 2) the peer selection method with theproposed preference appropriately reduces the high-cost transit
traffic with degree-based AS relationships inference heuristics
even though there is no public AS relationships information.
Index Terms—content delivery network, transit traffic, Internet
economics

I. I NTRODUCTION
Content Delivery Networks (CDNs) are widely employed
to distribute large content files such as music files, movie
files and software images. Various peer-assisted CDNs suchas BitTorrent [1] have been developed and deployed to avoid
excessive server load and to achieve effective and high-quality
content delivery over the Internet. Since the topology of peerassisted CDNs is generally different from the layer 3 network
topology and it is hard to take into account the interdomain
routing policies and economics, these CDNs frequently utilizea larger amount of network resources and cost more from thelayer 3 operators’ viewpoint [2], [3], [4].
The Internet consists of thousands of distinct administrative
domains called Autonomous Systems (ASes). Customer ASes
purchase Internet access over transit links from provider ASesby paying some amount of money according to their actual
bandwidth usage [5]. Therefore, transit traffic exchanged with
providers costs more for network providers compared to intradomain traffic or traffic exchanged over peering links. In
the interdomain routing, ASes control the transit traffic bya typical best path selection policy. However, in peer-assisted
CDNs, peers are distributed into many distinct ASes, and ASes

cannot control the peer selection because it is controlled bypeers. So, peer-assisted CDNs often utilize high-cost transit
links despite the existence of lower-cost delivery paths fromthe network providers’ viewpoint. Hence, the peer selection
control and the transit traffic reduction in peer-assisted CDNs
is required.
We have measured and analyzed peer distribution in BitTorrent [1], which is one of peer-assisted CDNs. From theanalysis, we show the potential of the transit traffic reductionin peer-assisted CDNs. We then propose an AS relationshipsaware peer selection preference to control and reduce the highcost transit traffic. Since most commercial Internet service
providers (ISPs) cannot disclose the relationships due to theircommercial contract, we employ degree-based heuristics for
inferring the relationships. Here, degree can be approximatedfrom publicly available BGP routing tables. We evaluate theproposed preference by trace-driven computer simulation, andshow that the peer selection method utilizing the proposed
preference reduces the transit traffic adequately.
The contributions of this paper are twofold:
1) We show the potential of the transit traffic reduction bypeer distribution measurement and analysis.
2) We propose a simple AS relationships-aware peer selection preference and the peer selection method with theproposed preference adequately reduces the transit traffic
without non-disclosure AS relationships information in
trace-driven simulation.
II. R ELATED W ORK
Round Trip Time (RTT) and router hop count have beenused in peer selection algorithms to achieve high-quality (i.e.,
low delay and high throughput) content delivery [6], [7].
Xie et al. [3] have proposed a peer selection algorithm
and traffic control architecture for the efficient intra-domain
and interdomain network resource utilization. Their proposal,
however, has focused mainly on the intra-domain traffic engineering for improving content delivery quality, and theirinterdomain traffic engineering approach pays little attention
to the economical relationships among ASes (i.e., transit or
peering). Furthermore, there are difficulties in disclosing themetric and integrating complex policies among ASes because
the metric in their approach is converted from non-disclosure
router configuration.

III. AS R ELATIONSHIPS AND C ONTENT D ELIVERY
In peer-assisted CDNs, contents are delivered over the Internet utilizing peer-to-peer technologies. The Internet consistsof thousands of ASes operated by many distinct administrative
domains such as ISPs, companies and universities. The routing
among ASes is determined by an interdomain routing protocol
such as BGP [8].
A. Interdomain Economics and Routing Policies
The economical relationships between two neighboring
ASes can be categorized into three types [9]; 1) transit, 2)
peering and 3) sibling. There are certain routing policies foreach type of the relationships. We describe these relationships,
economics and routing policies below.
1) Transit: An AS purchases access to the Internet fromanother AS by paying some amount of money according
to bandwidth usage. Hence, transit traffic costs more forcustomer ASes and they need to reduce this traffic. A transit
link from a customer AS to a provider AS and a link with
opposite orientation are called customer-to-provider (c2p) link
and provider-to-customer (p2c) link, respectively. A provider
AS exports its routes and its customer routes, as well as its
provider or peer routes [10]. On the other hand, a customer
AS exports its routes and its customer routes, but usually does
not export its provider or peer routes [10].
2) Peering: A pair of neighboring ASes can exchange traffic
directly and traffic exchanged between two peering ASes isfree of charge. A peering link is called peer-to-peer (p2p)link. A peer AS exports its routes and its customer routes,
but usually does not export its provider or peer routes [10].
3) Sibling: Multiple ASes can belong to same organization.
Even though each AS may be managed separately from perspective of network administration, traffic can be exchanged
among them without any payment. A sibling link is called
sibling-to-sibling (s2s) link. A sibling AS exports its routes
and routes of its customers, as well as its provider or peer
routes [10].
Additionally, there is a typical best path selection policy
when an AS receives multiple paths to an identical destination [11]; (a) highest priority for customer routes, (b) middle
priority for peer routes, and (c) lowest priority for provider
routes1 . If the priority of each candidate link is equal, one of
the shortest (i.e., minimum AS hop count) AS paths is selected.
If AS path length is also equal, we refer to the AS number of
its neighbors, then the route from the smallest AS number2 isselected.
These routing policies result in valley-free paths [8] when
we ignore s2s links. A valley-free path means that a path
between any two ASes first traverses uphill (c2p) links, goes
1 Sibling is a special relationship, and there is not a typical best pathselection policy. In the AS path computation (Section IV-A), we set the lowest
priority (i.e., lower than the priority for provider routes) for sibling routes.
2 Most implementations refer to router ID instead of AS number [12], but
we refer to AS number because the dataset we use in this paper does notinclude router IDs.

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Fig. 1. An example of valid and invalid paths on the valley-free path model.
The interdomain routing policy results in the valley-free paths. Path (b) isinvalid (does not follow valley-free path model) because AS64501 does notexport routes received from a provider AS64502 to another provider AS64503.
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(b) Routing in peer-assisted CDNs
Fig. 2. Routing characteristics. (a) On the Internet, all paths between any
two ASes follow interdomain routing policies because each AS operates its
AS border routers by itself. (b) In peer-assisted CDNs, paths between any twoASes also follow interdomain routing policies, but the path from a content toa peer is selected by peers, and consequently the selection is not controlled
by ASes.

across at most one peering link, and then traverses downhill (p2c) links. We show an example of valid and invalid AS
paths according to the valley-free path model in Fig. 1. Fromthe economical viewpoint of the valley-free path model, thesource and/or destination ASes possibly pay some amount ofmoney for their communication, and intermediate ASes haveno cost disadvantage.
B. Content Delivery PathAs described in Section III-A, there are certain policies
on the interdomain routing and paths between two any ASes
follow these policies on the Internet. However, in peer-assisted
CDNs, paths from a content to a peer in a certain AS isselected by peers, and consequently ASes cannot control theselection as shown in Fig. 2. Since transit traffic costs more
for customer ASes, the transit traffic reduction in peer-assisted
CDNs is essential for them.
We show three examples of possible content delivery paths
in Fig. 3. From the viewpoint of economics, path (a) in Fig. 3 isobviously the worst case among these three examples because
both of the edge ASes have to pay transit fee. Paths (b)
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Fig. 3. Three examples of possible content delivery paths and the types of AS relationships between the source and its neighbor, and the destination andits neighbor. Obviously, path (a) is the worst case in these examples because both source and destination exchange traffic with their provider ASes; i.e., boththe source and the destination ASes are customers and have a cost disadvantage. Path (b) is a better case compared to path (a) because one of the edges is
not customer and it does not pay transit fee for the content delivery. Path (c) may be better than path (b) because the source can acquire transit fee for thecontent delivery.
TABLE IT HE TYPE OF RELATIONSHIPS OF EDGE AS ES AND DELIVERY COST
ToRdst \ ToRsrc
p2c
p2p
c2p

p2c
N/A
N/A
−, +

p2p
N/A
0, 0
−, 0

c2p
+, −
0, −
−, −

Each element x, y (x, y ∈ { −, 0, +}) denotes that the cost of destination AS
and that of source AS are x and y. Symbol − means the AS can pay transit
fee, and symbol + means the AS acquire transit fee. Symbol 0 means the ASdelivers contents without any payment. N/A denotes the path does not follow
valley-free path model.

and (c) are better than path (a) because one of the edges is
not a customer and it does not pay transit fee. We cannot
definitely determine which path is better between paths (b)and (c), but path (c) may be better than path (b) because
one of the edges can acquire transit fee from its customer forthe content delivery. From the viewpoint of content delivery
quality, shorter AS paths are preferred because there is astrong correlation between AS hop count and Round Trip
Time (RTT), which is one of the indicators of path quality [13],[14]. We discuss this point in detail as AS path length vs.
economics in Section VI.
We list the type of relationships of edge ASes on possibledelivery paths following the valley-free path model and thedelivery cost in TABLE I. In this table, the symbols ToRsrc
and ToRdst denote the type of relationships from source AS
to its neighbor and that from destination AS to its neighboron the delivery path, respectively; for example, if ToRdst is
p2c, the destination AS is a provider of its neighbor on thedelivery path. TABLE I does not show quantitative transit
fee because there exist some charging policies [15] and thecharge fee is not flat to each inter-AS link. From TABLE I,
we can qualitatively know that paths which both edge ASes
are customers are the worst as we described with Fig. 3.
IV. P OTENTIAL OF T RANSIT T RAFFIC R EDUCTION IN
PEER - ASSISTED CDN S
To show the potential of the transit traffic reduction in
peer-assisted CDNs, we have measured peer distribution for a

TABLE II
T HE NUMBER OF LINKS AND THE PROPORTION FOR EACH RELATIONSHIP
relationshipsibling (s2s)peering (p2p)transit (p2c/c2p)#links
219
6142
66181

proportion
0.302%
8.47%
91.2%

content in BitTorrent [1]. We then analyze the distribution andcontent delivery paths by using the AS-level Internet topology
dataset [16].
A. Internet Topology Dataset
We employ “The CAIDA AS relationships dataset(10/08/2009) [16]” as AS-level Internet topology for the
analysis. The relationships in this dataset are inferred byalgorithms [17], [18] from collected AS paths. This dataset
includes 32281 ASes and 72542 inter-AS links. We write up
the number of inter-AS links and the proportion for eachrelationship included in the dataset in TABLE II; most of
the inter-AS links (91.2%) are transit, and a few links (only0.302%) are sibling.
We then compute AS paths for all ASes (i.e., paths between
two any ASes) according to the routing policies and the best
path selection policy described in Section III-A.
B. Peer Distribution Measurement
We had collected a list of peers from a tracker3 every
minute from 23/10/2009 to 19/12/2009 for the content: DebianLinux [19] DVD image; debian-503-i386-DVD-1.iso(4.4GB). The collected list includes sets of peer’s IP address
and port number.
We then aggregate one-minute peer lists into a five-minutepeer list to complement incomplete4 lists as shown in Fig. 4,
and extract unique IP address list from the lists and annotate
IP addresses with AS number by using a BGP routing table5
3 http://bttracker.debian.org:6969/announce

4 Not all active peers appear in a list because some peers are filtered out by
the tracker.
5 route-views2.oregon-ix.net archive at 01/08/2009.

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Fig. 4. Peer list aggregation procedure. One-minute peer lists in five minutes
are aggregated into a five-minute peer list.
TABLE IIIB REAKDOWN OF EDGE AS RELATIONSHIPS OF THE MESH PATHS AMONGAS ES ACCOMMODATING B IT T ORRENT PEERS
proportion
80.83%
11.99%
4.48%
2.70%

collected by Route Views Project [20]. We observed 48844
unique IP addresses belonging to 2569 ASes in the lists.
C. Content Delivery Path Analysis
To show the potential of the transit traffic reduction in peerassisted CDNs, we count the number of worst/better paths incase that all the ASes/peers connect each other (i.e., meshconnection).
First, we analyze the mesh paths among ASes whichaccommodate the peers observed during the measurement
period (58 days). The number of the paths amounts 6597192(= 2569 · (2569 − 1)) because the number of ASes whichaccommodate observed peers is 2569. We show the breakdownof edge AS relationships of these mesh paths in TABLE III.
This table shows that most of paths (80.83%) are the worst
paths from the economical viewpoint; i.e., both edge ASes
are customers. When we assume that the peers are randomly
selected, the probability that the worst paths are selected isvery high. Hence, there is a potential of the transit traffic
reduction by selecting better paths such as p2p–c2p (11.99%)
and p2c–c2p (4.48%) paths.

1

intra
c2p-c2pp2c-c2pp2p-c2p
otherproportion

0.8
0.6
0.4
0.2
0
07/Nov

14/Nov
21/NovTime (Year 2009)28/NovFig. 5. Temporal breakdown of edge AS relationships of the mesh paths(11/2009). For legend x–y, x and y denote ToRsrc and ToRdst , respectively. This
figure shows that the mean number of the worst paths (c2p–c2p) described in
Section V reaches 52.7%.

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ToRsrcToRdst
c2p
c2p
p2p
c2p
p2c
c2p
others than the above
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Fig. 6.

Example of peer selection preference

Second, we analyze the mesh paths among peers whichobserved in each five-minute peer list. We show the temporal
breakdown of edge AS relationships of these mesh paths
in Fig. 5. This figure shows that the worst paths from theeconomical viewpoint are dominant on every list, and the mean
number of the worst paths reaches 52.7%. Furthermore, since
few intra-domain paths exist on every list, interdomain traffic
control is effective for peer-assisted CDNs. Here, we note that
intra-domain traffic control is also important because the traffic
goes through each intra-domain network.
V. AS R ELATIONSHIPS - AWARE P EER S ELECTION
We showed the potential of the transit traffic reduction
in Section IV. We then propose AS relationships-aware
peer selection preference to reduce transit traffic exchanged
with provider ASes in peer-assisted CDNs. The relationships
among ASes, however, are generally non-disclosure information due to their commercial contract. Consequently, we employ degree-based heuristics [10] for inferring the relationships
in the proposed method. The heuristics is very simple; higher
degree AS is a provider and lower degree AS is a customer.
We do not use path analysis also proposed in [10] because
this requires lots of AS paths and it is difficult to collectthem in the peer selection procedure. On the contrary, degree
cam be approximated from publicly available BGP routing
tables, which is spanning subgraphs, such as Route Views
Project [20].
A. Peer Selection Preference
We define peer selection preference p of the path fromsource peer s belonging to AS S to destination peer dbelonging to AS D by Equation (1) according to TABLE I.
p (s, d) := (ρD − ρND ) + (ρS − ρNS )(1)

Here, ρ• denotes degree of AS •, and NS and ND are theneighbor of AS S and that of AS D on the delivery path,respectively. The first and second terms of Equation (1) meantransit cost for destination and source edges, respectively;i.e., if the term is negative number, the AS pays transit fee.
Consequently, a path with larger preference costs less for ASes
accommodating peers and is better.
We show an example of peer selection preference computation in Fig. 6. As shown in Fig. 6, larger number of preferencemeans better paths.

80



Fig. 7.



Peer selection methods for the evaluation

B. Evaluation
We evaluate the proposed preference by comparing with a
traditional peer selection method, which is minimum AS hop
count selection (AS-hops), through a trace-driven computer
simulation. Minimum AS hop count selection is the alternative
to the minimum router hop count selection since we do
not deal with intra-domain topology in the simulation. The
minimum router hop count selection is one of the common
approaches for traffic localization. To evaluate the influenceof the proposed preference appropriately, we refer to thepreference after minimum AS hop count selection in themethod with the proposed preference (Fig. 7); i.e., a peer
selects one which maximize the preference from candidate
peers minimizing AS hop count.
1) Trace-driven Simulation: We use the five-minute peer
lists described in Section IV-B for the simulation; the numberof lists is 8928 (i.e., 31 days from 31/10/2009 to 30/11/2009).
In this simulation, we use a communication model described
below.
Peers exchange 4457 pieces of the 4.4GB content (i.e.,the size of a piece is 1MB)6 over the topology describedin Section IV-A.
• The pieces are delivered in series. For example, a peer
starts downloading piece 2 after it completes the download of piece 1.
• A peer starts downloading the content (i.e., the first piece)
when the peer first appears in the list, and continue
downloading the content while the peer is in the list or
until the peer completes downloading whole the content.
For example, a peer first appears in the list of 01/11/2009
10:05, and then the peer starts downloading the first pieceat 01/11/2009 10:05. This means every peer downloads
the content once, and peers never delete pieces.
• A piece is provided by peers which have already downloaded the piece.
• A peer selects one of the peers which provide thepiece in the list according to the peer selection method.
When the selected peer disappears from the next list, thedownloading peer re-selects another peer.
• The peers measured on the first hour (31/10/2009 00:00–
01:00) have all pieces.
• Every peer has 1Mbps upload and download bandwidth.
Backbone networks have unlimited bandwidth.
•

6 This piece size follows the .torrent file.

from/to provider
from/to peer
from/to customer
percentage of interdomain traffic [%]

70
60
50
40
30
20
10
0

AS

-ho

ps

pro

po

se

d

Fig. 8. Breakdown of total exchanged interdomain traffic volume of theASes accommodating peers by types of AS relationships.
TABLE IV
M EAN THROUGHPUT
methodAS-hops
proposedthroughput [Kbps]
22.6
17.2

2) Results: We show the breakdown of total transferredinterdomain traffic volume of the ASes which accommodate
peers by types of AS relationships in Fig. 8. The method withthe proposed preference reduces transit traffic exchanged with
provider ASes by 8.46 percentage point compared to minimum
AS hop count selection. This result shows that even the simple
preference using degree-based heuristics achieves the transit
reduction.
However, the method with the proposed preference deteriorates the mean throughput as shown in TABLE IV. Thisis because the number of candidate peers decreases and the
distribution of selected peers concentrates. We discuss thispoint in Section VI as quality concerns and impact on users.
VI. D ISCUSSION
AS path length vs. economics: In Section III, we described
AS relationships and the economics. According to the best
path selection policy, the economical policy takes higher
priority than AS path length. Naturally, ASes can adapt thepolicy to cases because the routing is controlled by themselves.
Then, how about content delivery paths? In Section V, we use
the proposed preference after filtering out by AS hop count tocompare with minimum AS hop count selection. This is notconsistent with the interdomain routing policy. We consider
that AS path length, which is one of the quality parameters,
should take higher priority than economics in the peer selection by following two reasons: 1) The degree-based heuristics
possibly makes inaccurate inference, so quality deteriorationby this inaccurate inference should be avoided. 2) The peer
selection is not controlled by ASes but by peers, so quality
parameter should take higher priority. We will quantitatively

evaluate the balance between quality and delivery cost in
future, though we naively filtered out by AS hop count tocompare the proposed preference with minimum AS hop countselection in this paper.
How to provide degree database and look up AS path:
We proposed only peer selection preference and we do notmention the peer selection procedure. The proposed preference requires two types of information in the peer selection
procedure; 1) degree of ASes and 2) AS path. Currently, we
consider that the degree is provided by degree database server.
We will design the protocol to look up the degree in future. AS
path is easily resolved by network management tools such as
traceroute command in Unix/Linux system. Since thesetools are not applicable to some networks due to firewall etc.,
AS path look-up architecture is also problem to be solved.
Quality concerns and impact on users: This mechanism
as well as other localization techniques may ameliorate or
deteriorate content delivery quality. The localization seems
to ameliorate the quality because the distance for content
delivery becomes much shorter, but the distribution of selected
peers may converge more. It is easily to imagine that someother parameters such as the number of simultaneous uploads
would be effective for avoiding this convergence. Moreover,in our evaluation, the quality deterioration occurs because
backbone networks have unlimited bandwidth and bandwidth
for paths between any two peers is flat. In the real environment,
the bandwidth on backbone networks is also limited andthroughput decreases according to the distance between peers.
We will evaluate these environments in future.
VII. C ONCLUSION
We had measured and analyzed the peer distribution in
BitTorrent [1], which is one of peer-assisted CDNs. We
showed that most of the mesh paths among ASes were theworst paths from the economical viewpoint, and also showed
the potential of the transit traffic reduction from the peer
distribution analysis.
We then proposed an AS relationships-aware peer selection
preference to reduce transit traffic exchanged with provider
ASes in peer-assisted CDNs. We employed degree-basedheuristics for inferring non-disclosure AS relationships. We
evaluated the proposed preference by trace-driven computer
simulation and showed that the peer selection method withthe proposed preference adequately reduced transit traffic
compared to minimum AS hop count selection.
This paper delivers the following contributions; 1) we
showed the potential of the high-cost transit traffic reductionand 2) we proposed an AS relationships-aware peer selection
preference without non-disclosure AS relationships information, and we showed that the peer selection method with theproposed preference reduced transit traffic.
ACKNOWLEDGMENT
We especially thank Sho Fujita and Yosuke Himura for theirsupport and valuable advice.

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