Improving deployability of peer-assisted CDN
platform with incentive
Tatsuya Mori∗ , Noriaki Kamiyama∗ , Shigeaki Harada† , Haruhisa Hasegawa∗ , Ryoichi Kawahara∗∗ NTT Service Integration Laboratories, Midoricho 3–9–11, Musashino-shi, Tokyo, 180–8585 Japan{mori.tatsuya, kamiyama.noriaki, hasegawa.haruhisa, kawahara.ryoichi}@lab.ntt.co.jp† NTT West R&D Center, Shimaya 6–2–82, Konohana ward, Osaka-shi, Osaka, 554–0024 Japans.harada@rdc.west.ntt.co.jp

Abstract—As a promising solution to manage the huge workload of large-scale VoD services, managed peer-assisted CDN
systems, such as P4P [25] has attracted attention. Although theapproach works well in theory or in a controlled environment,
to our best knowledge, there have been no general studies that
address how actual peers can be incentivized in the wild Internet;
thus, deployablity of the system with respect to incentives tousers has been an open issue. With this background in mind, we
propose a new business model that aims to make peer-assisted
approaches more feasible. The key idea of the model is thatusers sell their idle resources back to ISPs. In other words,
ISPs can leverage resources of cooperative users by giving them
explicit incentives, e.g., virtual currency. We show the high-level
framework of designing optimal incentive amount to users. We
also analyze how incentives and other external factors aﬀect theeﬃciency of the system through simulation. Finally, we discuss
other fundamental factors that are essential for the deployabilityof managed peer-assisted model. We believe that the new businessmodel and the insights obtained through this work are useful forassessing the practical design and deployment of managed peerassisted CDNs.

I. Introduction
A. Rising bandwidth scavenger and existing solutions
Recent growth in large-scale video sharing services suchas YouTube [26] and MSN Video [18] have been significant.
These services are estimated to facilitate hundreds of thousands of newly uploaded videos per day and support hundredsof millions of video views per day. The content available onthese services are known as user-generated content (UGC)
and are posted by end-users. Besides UGC, professionallygenerated video on demand (VoD) services, have also beensuccessful. For example, in Feb 2009, a web video site
Hulu [15] streamed about 18 million videos a day [10].
The great popularity of these VoD services has lead to ashift in Internet traﬃc mix. It is reported [8] that P2P traﬃcshare dropped to 51% at the end of 2007, down from 60%
the year before, and that the decline in this traﬃc share isdue primarily to an increase in traﬃc from web-based video
sharing services. It is also reported in 2008 [3] that statistics
on one million broadband users in North America showed that
HTTP traﬃc accounts for 46% of all broadband traﬃc while
P2P applications now account for only 37%. We envision that
this trend will potentially keep growing; thus, managing thehuge volume of video traﬃc will continue to be a challenging
task for both content providers and ISPs.

As a promising solution to address the huge workload oncontent providers, peer-assisted VoD systems, which leverage
P2P technology, are now attracting attention. Recent studies
on the world’s largest commercial VoD services, e.g., YouTube
and MSN Video, concluded that peer-assisted solutions havea great advantage in oﬄoading the workload of content delivery servers [5], [11], [14], [28]. Peer-assisted VoD delivery
architectures have been widely studied [6], [20], [22]. In
the commercial market, some content providers have been
deploying peer-assisted video services [4].
The peer-assisted solutions are greatly successful in reducing the workload on content providers; however, theypotentially cause an inevitable problem. That is, although
P2P’s flexibility in choosing peers benefits the robustness andscalability of content distributing systems, if not carefully
designed, it scatters traﬃc globally without consideration of
the underlying network structure, resulting in an increase incross-ISP traﬃc. Thus, peer-assisted solutions could impose
amplified traﬃc on the external backbone links of ISPs.
To avoid this problem, it is crucial that ISPs localize P2P
traﬃc. Managed peer-assisted approaches have been extensively studied in various ways [1], [2], [7], [16], [17], [25]. Xie
et al. [25] recently proposed a fundamental framework, called
P4P, that localizes P2P traﬃc by providing P2P applications
with network information such as topology and link status.
The goal of the P4P framework is to build interfaces toenable P2P applications and networks to communicate witheach other and establish optimized peer selection. Choﬀnes etal. [7] demonstrated the eﬀectiveness of biased peer selection,
which leverages Content Delivery Network (CDN) redirection
information, in reducing cross-ISP traﬃc. The problem oftraﬃc localization is discussed in the broader problem space– the Application-Layer Traﬃc Optimization (ALTO), which
addresses the way to provide the topology of the underlying
network while allowing the requesting node to use suchinformation to eﬀectively reach the node that has content [17].
This problem is now being discussed in the IETF.
Although the state-of-the-art approach mentioned above,i.e., managed peer-assisted CDN (MP-CDN in short), works
well in theory or in a controlled environment, to the best of
our knowledge, there have been no general studies that address
how actual peers can be incentivized in the wild Internet; thus,
deployablity of MP-CDN with respect to incentive has beenFig. 1.

Business model of the three players.

an open issue [21], [23]. The question we address is:“What is the motivation for peer nodes to participate in thesystem and contribute their resources?”
Rodriguez et al. [23] pointed out that in addition to thecurrent incentive mechanism in the layer of P2P applications,
i.e., nodes are required to contribute to the system roughly as
much data as they want to download, other form of incentives
such as economic savings, should be explored in depth toestablish feasible P2P CDN services. Rajahalme et al. [21]demonstrated that it is essential that caching incentives should
exist to achieve data-oriented networking, where caching data
plays a central role. We note that some applications, such as
BBC iPlayer [4], embed uploading function implicitly, i.e., theresource of a user is agnostically leveraged in the system when
the user is using the application. However, it is not clear that
end-users will be willing to accept the implicit contribution in
general.
B. Our solution: a new business model
To address the incentive problem in the MP-CDN framework, we propose a new business model for eﬃcient andinexpensive delivery of large-scale traﬃc. The key idea of
our model lies in the new incentive structure among the three
key players, user, ISP, and content provider. Our objective isto encourage these players to work eﬀectively by leveraging
an incentive model that does not exist in the current Internet
business model.
Figure 1 illustrates the business model among the three key
players. An ISP can make use of user resources by giving them
explicit incentives, e.g., giving virtual currency or discounting
monthly fee, etc. An explicit incentive will clearly motivate
users to contribute their resources as a part of the CDN system
operated by an ISP. The ISP in turn can manage traﬃc flows by
fully controlling the user resources with the knowledge of theexact information of peers and their files, network topology,
routing matrix, and link congestion status.
We show how the model benefits the three players. First,
end-users can get additional bonus by selling their idle resources. As a consequent, they can also enjoy the high qualityof experience (QoE) and high availability of the content files
that are cached on a large number of distributed peer nodes in
the ISP. This idea may look like a business model of modern
electrical grid system, where home owners install small power

generation systems such as solar panel or wind turbine, andthey are allowed to sell the electricity back to the grid for thesame price they would pay for it.
Second, leveraging user resources enables an ISP to establish eﬃcient traﬃc management. Reducing cross domain traﬃcsaves transit cost on the ISP’s external links. It also allows
the ISP to control the timing of installing the new network
line cards on the high-end boarder routers by cutting verbose
cross-domain traﬃc. Traﬃc management also enables the ISP
to avoid creating hot spots in its network and improve theQoE of the content delivery service. By oﬀering high-quality
and low-cost content delivery service, the ISP will be able tohave new business opportunities with content providers withthis model. Thus, the ISP can amortize the cost of incentive
for users.
Finally, it enables a content provider to oﬄoad the workload
on its origin servers because the ISP can localize many user
requests within its network. Moreover, if a content provider
intents to provide the services to users of the ISPs in a
specific region, e.g., in a specific country, the content provider
may be able to expect the large reduction in cost of using
CDN platform because ISPs with MP-CDN need to prepare
resources within their networks while conventional global
CDN companies need to prepare the resources in all over theworld.
Thus, the new business model can establish a “win-winwin” situation among the three players. Rather than imposing
a bandwidth cap to control traﬃc from a bandwidth scavenger,
which is a user or an application that sends out massive amountof traﬃc, as many ISPs are doing today, promoting the new
incentive model will help evolve emerging Internet services
and opportunities.
The remainder of the paper is structured as follows: In
section II we show a model of MP-CDN we consider in thiswork. In section III, we show a high-level framework fordesigning optimal incentives while keeping a good balanceof cost and revenue of an ISP. In section IV, we analyze thetrade-oﬀ between system performance and cost for incentive
through a simulation. we also study how other external factors
aﬀect the performance of MP-CDN. Section V discusses otherpotential factors that are important for the deployability of MPCDN. Finally, section VI summarizes the work and discusses
future work.
II. Model of MP-CDN
In this section, we describe an overview of the basic MPCDN system. Figure 2 illustrates the model of an MP-CDN.
In the model, an ISP manages a server, which is called a PM
(peer management) server. As we shall discuss later, the ISP
can build multiple PM servers to avoid creating a single pointof failure. A PM server plays a central role in the system andhas the three fundamental functions, i.e., cache management,
accounting, and peer selection.
cache management: As the central servers in centralized P2P
networks, such as Napster [19] and eDonkey2000 [9], a PM
server maintains a list of connected nodes and the files theykeep in their storage. First, peer node A sends a data request

for Internet access service while the latter is from a content
publisher for outsourcing CDN service. Here, we assume thatboth incomes are given as a function of N; i.e., we have
R = f (N).
We also assume that the cost for the ISP, C, is the summation of the bandwidth cost, Cb , cost of user incentives Ci , andremaining costs, Cr ; i.e., C = Cb + Ci + Cr . The ISP needs toensure that the revenue is larger than cost to make the service
sustainable; i.e.,
R ≥ Cb + Ci + Cr .
(1)
Fig. 2.

Model of MP-CDN.

to the PM server. If PM server finds node B that has the file,the PM server will notify node A to grab data from node B.
Otherwise, the request will be forwarded to the original server.
Thus, the PM server provides a function of cache management,
where the virtual cache server is built on top of storage devicesof participating peer nodes. We note that achieving a goodcache hit rate is the key success factor for the business model.
accounting: All peer nodes in an MP-CDN send the status of
their connections to the PM server, e.g., completion of downloads/uploads. Thus, a PM server can keep track of the amountof contributions made by each peer. The incentive amount toeach user can be determined based on the information about
their contributions.
peer selection: Like the peer selection mechanism proposedby Xie et al. [25], when a PM server detects multiple nodes
that have a content file requested by a peer, the PM server
determines the peers following its own criteria; e.g., select theones that can localize traﬃc the most. This mechanism allows
the ISP to lower cross-domain traﬃc, which results in eﬃcientuse of the internal network.
III. Designing optimal incentive
The primary goal of an ISP that deploys MP-CDN withincentive is to achieve a certain amount of cache hits, while
spending the least for incentives. The MP-CDN will achieve
good cache hit ratio if the ISP can increase the number ofparticipating peers. On the other hand, the increase in thenumber of peers results in an increase in the cost of the
incentives to peers. Thus, the two requirements are in a tradeoﬀ relationship.
In this section, we show a high-level framework of designing the optimal incentive amount to users under the tradeoﬀ. Through the framework, we attempt to demonstrate thatan ISP is able to design optimal incentive under a certain
condition. In the following analysis, we are interested in themacro property of the system rather than the micro behaviourof particular nodes or files. We assume that there is no cache
capacity limitation and we are interested in an asymptotic but
not transient status. More detailed analysis on caching property
will be discussed in the next section.
Let N be the number of ISP users. The amount of total
revenue for the ISP, R, is the summation of the ISP service
and CDN service incomes. The former is from ISP users

Let n(x) be the number of peer nodes who are willing toprovide their resources as part of the P2P platform with
the incentive of x. In general, n(x) may be modeled as anincreasing function of x. For simplicity, we assume that the
ISP can use all n(x) nodes as the CDN platform. That is, all
the n(x) nodes are available 24/7.
For simplicity, we assume that all traﬃc is generated by the
CDN service. Then, the cost Cb is given as a function of b,which is the amount of bytes generated by the CDN service.
The cost of incentives to users is the product of x and n(x).
To summarize, we have Cb = g(b) and Ci = xn(x).
The total amount of traﬃc in bytes carried over the CDN
service, b, is given as the function of x, b = (N − n(x) + 1) vu ,where vu is the average traﬃc volume carried to a user overa time period. Note that the amount of traﬃc decreased bypeer nodes; i.e., once a peer node downloads a content, othern(x) − 1 peers download the content within the P2P network.
This is localization of traﬃc. Note also that we assume thepeer nodes are fixed in the time period.
From Eq. 1, we have the following inequality:f (N) ≥ g(b) + xn(x) + Cr .

(2)

Our goal is to find x that minimizes C under the conditionof Eq. 2. Next, we explore the model with the simplest formof g(b) and n(x), i.e., g(b) = θb and n(x) = px1 . The first
Eq. assumes that the cost of bandwidth is proportional to theamount of bytes carried over the external link, and θ is thecost required to carry a byte. The second Eq. assumes that the
number of cooperative users is proportional to the amount of
incentives. We note the n(x) should not exceed N, i.e., x ≤N/p. Eq. 2 becomes the simple quadratic inequality: px2 −pcu x + (N + 1)cu + Cr − f (N) ≤ 0, where cu = θvu is theaverage bandwidth cost per user. Since the left term of theinequality is concaved upward, the solutions of the inequality
exist only if f (N) ≥ −pc2u /4 + (N + 1)cu + Cr .
Under the above condition, the optimum incentive amountx = x∗ that minimizes the total cost is given as x∗ = cu /2.
Thus, in this model, the optimum amount of incentive to auser is given by half of the average bandwidth cost per user.
We note that our objective here is to demonstrate a highlevel framework of extracting optimal incentive amount under
1 We note that these assumptions are very naive. For example, the traﬃccost model of an ISP can be stepwise functions instead of the linear function
shown here. However, our goal is to show a high-level view of the economic
structure using a simple model. We will discuss more sophisticated model forthe acceptance of incentive in the next section.

a certain condition, rather than extracting a closed form of the
optimum incentive amount in a specific condition. Once we
fix the model of the system and other external factors, suchas expected revenue and cost of the service, we can derive
the optimal incentive in a closed form or numerically with theframework shown in this section.
IV. Analysis of cache performance
In this section, we study how incentives and other external
factors aﬀect the cache performance through a simulation.
As we described in the previous section, getting good tradeoﬀ between incentive and cache performance, i.e., lowering
cost of carrying traﬃc, is a key success factor for feasible
deployability of MP-CDN.
We first describe the details of the MP-CDN model we
use in our simulation. We then show the intrinsic trade-oﬀ
between cache performance and cost of incentives. Finally,
we study how the trade-oﬀ is aﬀected by external factors suchthe number of nodes, the number of distinct content files, andstorage capacity for each node.
A. ModelAssumptions: We use the minimal model in the simulation(see Fig. 2). To focus our attention on the analysis of cacheperformance, which plays a crucial role in establishing eﬃcient
network utilization, let’s assume that there are no network
resource constraints, e.g, bandwidth, on the internal network
entities. Thus, the peer selection in an ISP is performed
based on the content possession information rather than theunderlying network information. The objective of a PM serveris simply to increase cache hits with least cost of incentives.
We also make the following assumptions in the model:• The properties of nodes are identical.
• Each node selects a content file according to the stretchedexponential distribution [12]. (see below for more detail)• The nodes are incentivized according to the univariate
logit model. Only incentivized nodes will contribute to
uploading their files. (see below for more detail)• The incentive amount to users is fixed, regardless of theamount of contribution from each user.
• The nodes keep limited number of content files with theleast frequently used (LFU) cache algorithm, which ismanaged globally by the PM server; i.e., the frequency
information is provided by the PM server.
• The nodes do not send requests for files they have alreadydownloaded and not removed.
• The number of nodes and content files are fixed, i.e., there
are no churns in the simulation.
• The size of content files is fixed.
Although we could use more realistic and complicatedmodel here, e.g., heterogeneous setting of node properties, wefirst focus our attention on the macro relationship between the
incentive and the cache performance on the system. In our
future work, we will study how heterogeneity of nodes could
aﬀect the workload on each user etc.
File access pattern: For the file selection model, we adopt thestretched exponential distribution, which can well capture theaccess pattern of various modern media services such as web,YouTube, VoD, and IPTV, that benefit CDN architecture [12].
The probability distribution of the stretched exponential distribution is given as Pr [X < x] = 1 − exp (− (x/x0 )c ) , wherec is the shape parameter and x0 is the scale parameter.
The smaller the shape parameter, c, the more biased theaccesses to the most popular content files. The probabilitythat ’i’th media
is selected,
1/c pi , is cgiven as pi c = yi / i yi ,where yi = −a log(i) + b , a = x0 , and b = y1 . Following
the statistics of CTVoD-04 discussed by Guo et al. [12],which is derived from a study on a large-scale VoD system
deployed by China Telecom [27], we adopt the parameters of
a = 12.96, b = 118.9, and c = 0.4.
Acceptance of incentive: For the incentive models, we used
the logit model. The logit model is a well known approachin various field for modeling the probability of a customer
accepting a new product or technique [24]. The advantage ofadopting the logit model lies in its simplicity. Once we havethe survey data on users, we can easily perform preference
regression on the model with explicit measures of confidence
level and significance.
Given incentive of x, a node becomes cooperative to thesystem with the probability of p(x), such that p(x) = 1/(1 +e−(β1 +β2 x) ), where β1 = − log(1/ξ − 1), β2 = log(1/ξ − 1)/α, andξ is the probability that a node is cooperative without having
incentive (i.e., p(0) = ξ). α is the mean of x required to makenodes cooperative. In this work, we use ξ = 0.01 and α = 10.
Simulation setup: We performed 10 experiments with independent random seeds for each parameter setting. Let N be thenumber of end-hosts, m be the number of content files, andS be the storage capacity for each node with respect to thenumber of files (note that the file size is fixed in this work),
respectively. The end-hosts independently generate requests
with exponentially distributed inter-arrival time. The meaninter-arrival time is set to 72. Note that a unit of simulation
time is roughly assumed to be five minutes in real time; i.e., themean inter-arrival time is set to 6 hours in real time. Similarly,
the time to finish downloading/uploading a file is set to 6,
which is 30 minutes in real time. Note that we assumed thatthe size of content files is fixed and there is no bandwidthbottleneck in the network. To see the asymptotic propertyof the system, the simulation time was set to 10000, whichroughly corresponds to a month in real time. Figure 3 shows
an example of a simulated path.
B. Trade-oﬀ between cache performance and cost of incentive
We studied the trade-oﬀ between the cache performance andcost of incentives. Figure 4 shows the results. The parameters
were set to N = 100, m = 1, 000, t = 10, 000, and S = 5. Figure 4 (a) shows the number of incentivized users, N(x), with
the incentive per user, x. Obviously, the shape of N(x) comesfrom the sigmoidal function of the logit model. Accordingly,
the cache hit ratio shown in Fig. 4 (b), which is defined as thenumber of cached downloads divided by the total number ofdownloads, behaves in a similar manner. That is, the increasein the number of incentivized peers corresponds to the increasein the storage space that can be used for caching.

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#nodesFig. 5. Eﬀect of the number of total nodes in the system: Left: Cache hit
ratio; Right: total cost of incentive. Circles represent each simulation run (10for each parameter setting) with each line representing the mean values.

iteration

(a)

(b)

100

0.4

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hit ratio#incentivized peers

Fig. 3. Asymptotic property of system behaviour. Parameters are set to:
N = 100, m = 1000, and S = 5. Top: Number of total downloads; Middle:
Number of cached downloads; Bottom: Cache hit ratio, i.e., number of cached
downloads divided from total number of downloads.

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Next, Fig. 4 (c) shows the total cost of incentive, Ci , i.e.,
Ci = xN(x). The total cost increases non-linearly when x < 20
and increases linearly otherwise. Figure 4 (b) and (c) show thetrade-oﬀ between the cache performance and cost of incentive.
To see this trade-oﬀ more clearly, Fig. 4 (d) shows how the cost
factor is aﬀected by incentive. The cost factor C f is defined
as C f = Ci − θT c , where T c is the amount of traﬃc savedby the cache, θ is the weight parameter, and T c is given by
the number of cached files in the system. As we showed in
section III, these factors are essential for the balance of total
revenue and cost. We can see that for each weight, there exist
an amount of incentive that minimize cost factor C f .

20incentive

30

0
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θ = 0.5
θ = 1.0
θ = 2.0

-2000
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incentive

Fig. 4. Eﬀect of incentive: (a) Number of incentivized peers, (b) Cache hit
ratio, (c) Total cost of incentive, and (d) Cost factor. Circles indicate eachsimulation run (10 for each parameter setting) while lines indicate the meanvalues.

Although the behaviour of N(x) and the cache hit ratio looks
similar, we note that the relationship between the two is nottrivial. The reason is as follows. Each node independently
requests files and manages the cache space with the LFU
algorithm, following the perfect file access frequency information provided by the PM server. Thus, the globally managed
cache can keep duplicated files in the aggregated cache space.
Therefore, the established cache performance by the MP-CDN
model is diﬀerent from the one for the LFU cache deployed
in a single server, for which case, the cache does not keep the
duplicated files and the theoretical cache performance can becalculated from the file access pattern and the available storage
space. We also notice that after all the peers are incentivized,
i.e., when x ≥ 20, the cache performance will no longer be
improved.

This section studies how other external factors such as thenumber of nodes, the number of distinct content files, and storage capacity for each node aﬀect the cache performance. The
total cost of incentive under the conditions is also explored.
Number of participating nodes: Figure 5 shows the meancache hit ratios under various number of nodes, N (0 ≤N ≤ 104 ), in the system. The amount of incentive per user
is set to x = 10, the number of content files is set tom = 1000, and storage capacity per node is set to S = 5. Bothcache hit ratio and cost of incentive increase as the numberof participating nodes gets higher. Obviously, total cost of
incentive is proportional to the number of nodes. In contrast,
the cache hit ratio gets higher rapidly when the number ofnodes is fairly small, say, N ≤ 1000. For example, for thenumber of nodes N = 500 and N = 1000, the mean cache hit
ratios are 0.53 and 0.67, and total cost of incentive are 2459
and 4957, respectively. In this case, 14% of improvement incache hit ratio needs to double the cost of incentive. These
properties play an important role for an ISP in balancing thescale of the users (nodes) and total cost of incentive. We notethat an ISP is able to control the number of incentivized nodes
by adjusting the amount of incentive per user as well.
Number of content files: Figure 6 (a) shows the mean cache
hit ratios under various number of content files, m (1 ≤ m ≤104 ). Here, the amount of incentive per user is set to x = 10,
the number of nodes is set to N = 100, and storage capacity
per node is set to S = 5.

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Fig. 6. Eﬀect on cache performance: the number of distinct content files inoriginal server (left) and the amount of storage capacity, S per user (right).
S = 0 stands for the infinite amount of storage space. Circles indicate eachsimulation run (10 for each parameter setting) with each line representing themean values.

Note that the total cost of incentive is independent of m.
As we can see, if original server has a lot of distinct content
files, the cache hit ratio gets worse under a fixed amount ofstorage space per user, i.e., S = 5 in this case. That is, when
there are a large number of distinct content files, we have alarge number of frequently accessed files as well. Therefore,
if the total amount of storage capacity is smaller than thenumber of these popular files, a probability of cache missincreases. Thus, to achieve high cache performance under agiven storage amount, it is an eﬀective strategy to keep thenumber of available content files fairly low within an interval.
We also notice that cache performance gets bit worse when mis very small, say, m = 1. This comes from the fact that it isassumed that nodes do not request files already downloadedby themselves before. Thus, the number of total downloads
gets smaller and the probability that a request hits the cache
becomes smaller. We note that the degradation is negligible
and we do not need to use MP-CDN when m is quite small.
Storage capacity of users: Figure 6 (b) shows the mean cache
hit ratios under various storage capacity per user, S (0 ≤ S ≤30), where we denote infinite capacity as S = 0. The amountof incentive per user is set to x = 10, the number of nodes
is set to N = 100, and the number of content files is set tom = 1000. As is expected, the cache performance gets betteras the increase in S . The good news here is that the cacheperformance with infinite storage space can be established withfairly small storage space, say S = 20. Assuming that the sizeof each content file is 2 GB (for example, the size of a 2hours movie file encoded at the mean bit rates of 2,000 Kbps
with a frame size of 640 × 480 pixels can be 2 GB), peer
nodes need to reserve storage capacity of 40 GB, which may
be aﬀordable amount of resource on a commodity PC today.
This good property reflects the potential power of peer-assisted
CDN, i.e., even though the resource of each node is not large,
the aggregated resource can become very large.
V. Detailed Discussion
Finally, we discuss other potential factors that could significantly aﬀect the deployability of MP-CDN services.

A. Scalability
An MP-CDN establishes a content delivery mechanism thatis eﬃcient in terms of reducing workload on servers andnetwork traﬃc across ISPs. Participating users benefit fromhigh performance, availability, and rewards from the ISP, while
the ISP benefits by cutting the cost of network traﬃc. Content
providers also benefit from the lowering of server workload.
What is the compensation for these benefits? The trick is in therole of the PM server, which acts as an oracle in the network.
That is, the PM server keeps track of nodes, content files,
and underlying network information such as network topology,
routing information, and link statistics. The use of a centralized
PM server that indexes all content might introduce a single
point of failure. Therefore, an MP-Codes success lies in thescalability of the PM server.
ISPs need to deploy a PM server as essential infrastructures, such as DNS cache servers or web servers, which also
have a risk of being a single point of failure if they are
managed inappropriately. There are a large number techniquesthat establish the high scalability of large-scale servers: loadbalancing, high-performance computing, and DHT. We willneed to leverage some of these approaches to make MPCDN scalable. Optimizing database design, processing, andconfiguration are also challenging and crucial tasks.
We finally note that the system is especially well suited
to supporting downloads of large files, so that the numberof downloads supported by PM server may not be that large
although the total number of bytes delivered by each peer node
may be very large.
B. Underlying network infrastructure
One assumption taken for granted in this paper is that transferring of data between peers within an ISP is a traﬃc patternthat the ISP’s infrastructure is designed to eﬃciently support.
It may well be that once an ISP goes through the eﬀort ofestablishing PM servers, it may place less stress on the network
to deliver all content from these servers to end users rather thanfrom peer to peer. However, if a network has been designed
to provide more download capacity to end users than upload
capacity, e.g., CATV access network, then the uploading offiles by peers might cause congestion in the network. We
believe that as far as an ISP can take control over selecting
peers with the exact knowledge of its network property andstatus, traﬃc optimization should work eﬀectively, given anunderlying network infrastructure. For example, we can assign
pair of peers so that traﬃc flows do not create hot spots inthe network. We can also adaptively utilize caching servers
if we see congestion on upload links. Moreover, as Fiber tothe premises/home (FTTP/FTTH) services become popular innear future, the problem of asymmetric network resources will
be naturally solved.
C. Privacy
As with many other web caching systems, an MP-CDN
operator has to make sure to protect the privacy of users.
In general, network administrative organizations have a clear

privacy policy for the use of web caching systems. The contents and logs on the cache servers will be properly managed,
following the privacy policy. Here is a question we want to ask:
How can we make sure that privacy is protected on the endnodes? Unlike network operators, end-users do not have themapping of IP addresses and actual user identities in general.
Thus, the privacy problem should be somewhat relaxed at thislevel. However, continued accesses from a fixed node could
expose intrinsic features that can be associated with a specificuser. To cope with this potential problem, an MP-CDN can use
probability to introduce weak randomness in the peer selection
mechanism, i.e., given a set of nodes with the same network
cost, a PM server will randomly pick one from the candidates
so that privacy is kept among end-users.
D. Toward more eﬃcient content delivering
Like many other P2P solutions, an MP-CDN can also
benefit from the strategy of splitting large files into chunks,
allowing nodes to retrieve diﬀerent chunks from diﬀerent
nodes. When a PM server receives a request of a content
file from node A, the PM server can reply to node A that
node A can download pieces of the content file from a setof multiple nodes. Although this strategy of pipelining will
potentially accelerate the eﬃciency of downloads, it could
also significantly increase the number of transactions between
nodes. As Harma and Felber [13] suggested, the trade-oﬀ
between the number of transactions and the size of chunks
should be carefully designed based upon the required goal
and resources. We will leave this as our future work.
VI. Conclusion and Future work
We presented a new business model that aims to make an
MP-CDN more feasible. The key idea is to allow ISPs toprovide end-users with direct incentives, which may encourage
the users to contribute their resources. The business model
enables a “win-win-win” situation among the three key players, user, ISP, and content provider. That is, the new approachrelaxes the intrinsic constraints of the current Internet businessmodel. We believe that the new model will shed new lighton emerging Internet services and opportunities not only inbusiness but also in technical fields. We presented a highlevel framework for designing an optimal incentive amount forusers. We also researched into the intrinsic trade-oﬀ between
the performance of system and cost for incentive, and the otherexternal factors on the cache performance through a simulation
study. Finally, we discussed other potential factors that are
essential for deploying managed peer-assisted CDNs.
Our future work includes: 1. enhancing the analysis formore realistic cases, i.e., heterogeneity of peer characteristics
and internal network topology, having constraints on network
resources, and more realistic model of traﬃc cost of an ISP,
e.g., the stepwise functions, 2. formulating cache performanceof an MP-CDN mathematically, and, 3. studying the behaviourof actual users and system performance, using actual implementation in a testbed network.

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