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P4P: Explicit Communications for Cooperative Control Between P2P and Network Providers
January 08, 2008 | By Yale
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Transcript
P4P: Explicit Communications for Cooperative Control Between
P2P and Network Providers
Haiyong Xie

Arvind Krishnamurthy
?
Avi Silberschatz

Y. Richard Yang

?
University of Washington

Yale University
Abstract
The emergence of peer-to-peer (P2P) is posing signifi-
cant new challenges to achieving efficient and fair utilization
of network resources. In particular, without the ability to
explicitly communicate with network providers, P2P applications depend mainly on inefficient network inference and
network-oblivious peering, leading to potential inefficiencies
for both P2P applications and network providers. In this paper, we propose a simple, light-weight architecture called
P4P to allow more effective cooperative traffic control between applications and network providers. Our evaluations
show clear performance benefits of the framework.
1 Introduction
A basic problem in a network architecture is how network
applications (i.e., network resource consumers) efficiently
utilize the network resources owned by network providers.
We refer to this problem as the network efficient traffic control problem, or traffic control for short. This problem is particularly important as it can have significant impacts on application performance, network provider efficiency and economics, and overall system complexity.
In the current Internet, for traditional point-to-point applications, efficient traffic control is largely determined by
network providers alone: applications specify only the destinations of traffic; it is up to the network to control both the
paths taken by the traffic and the transmission rates (through
TCP feedback) on the chosen paths. Network providers can
therefore improve efficiency unilaterally according to their
objectives. Specifically, providers can use optimal traffic engineering to determine efficient routing and satisfy economical objectives such as implementing valley-free routing.
However, the recent emergence of P2P applications is
posing significant challenges to efficient traffic control, with
neither the network nor the P2P system having complete
leverage over system efficiency.
First, for intradomain, the network-oblivious peering
strategy of many P2P applications may cause traffic to scatter
and unnecessarily traverse multiple links within a provider’s
network, leading to much higher load on some backbone
links. A recent study [13] estimates that the aggregated traf-
fic of all P2P applications contributes to about 50-80% of
the traffic in many networks. This increasing P2P traffic has
severe negative implications (see, e.g., [11, 21]).
Second, for interdomain, network-oblivious peering
may cause a P2P application in a non-tier-1 network
provider to relay a substantial amount of traffic between its
providers [17]. This may lead to serious disruption of ISP
economics. For example, in recent studies [5, 20] on Skype,
the authors found that many universities (aka edge ISPs) are
hosting a large number of Skype super nodes. Thus, they
handle a large amount of transient traffic from and then to
their providers, violating valley-free routing and leading to
substantially higher operational cost. Even for tier-1 ISPs
who do not make payments to network providers, P2P traf-
fic may cause traffic imbalance between its peers, leading to
potential violation of peering agreements.
Third, P2P’s dynamic traffic distribution patterns do not
necessarily enjoy a synergistic coexistence with network
traffic engineering [10, 16] – network providers go to great
lengths to estimate traffic matrices and determine routing
based on them, but all of this effort could be negated if
P2P applications modify their download behavior to adapt
to changes in the network, thereby resulting in oscillations
in traffic matrices and sub-optimal routing decisions.
In response to these kinds of issues, network providers
have considered multiple new traffic control techniques. Unfortunately, none of them appear to be fully satisfactory –
without P2P cooperation, the new techniques are either ineffective or degrade P2P performance and often times are
too complex. One approach, for example, is to install P2P
caching devices to cut down bandwidth consumed by P2P
applications (e.g., [6, 7, 18, 19]). However, these caches
need to be designed for specific applications and speak the
appropriate protocol, limiting their generality and applicability to closed protocols. Another technique is to deploy traffic
shaping devices to rate limit P2P (e.g., [2, 3]). These devices
rely on deep packet inspection or other P2P traffic identification schemes. However, different P2P protocols use different
control messages, and many P2P protocols use encryption
and dynamic ports to avoid being identified. It remains unclear whether in the long run traffic shaping can effectively
control the bandwidth consumption of P2P applications and
reduce provider’s operational costs. Furthermore, unilateral
rate limiting by network providers may substantially degrade
P2P performance and be at odds with consumer’s needs.
With network provider solutions being ineffective, a few
P2P systems have begun to investigate self-adaptation techniques, such as considering locality in peering (e.g., [7, 12]),
in order to achieve efficient traffic control. Although suchtechniques have the potential to improve both network effi-
ciency and application performance in certain settings, there
are fundamental limits on what P2P can achieve alone. In
particular, since traditionally traffic control is primarily performed by network providers, the current network architecture supports only implicit communications between applications and networks. Thus, to improve efficiency, P2P applications will have to depend on reverse engineering to determine network information such as topology, status and policies. However, this is challenging if not impossible.
Overall, the P2P paradigm exposes a fundamental issue
in traditional traffic control: emerging applications can have
tremendous flexibility in how the data is communicated, and
thus, they should be an integral part of network efficient control. However, if end hosts are to participate in network resource optimizations, then the networks cannot continue to
be opaque but need to export their status information.
We propose a flexible framework named P4P to enable better cooperation between P2P and network providers
through explicit communications. Here P4P stands for proactive network provider participation for P2P, or provider portal for P2P. The objectives of P4P are to (1) facilitate network
applications, in particular P2P applications, to achieve the
best possible application performance under efficient and fair
usage of network resources; and (2) allow network providers
to achieve efficient and fair usage of their resources to satisfy
application requirements, reduce cost, and increase revenue.
Note that although our presentation focuses on P2P, it can be
extended to other network application paradigms.
2 The P4P Framework
The P4P framework is a flexible and light-weight framework that allows network providers to explicitly provide
more information, guidelines and capabilities to emerging
applications, such as P2P content distribution.
2.1 Motivation
We now motivate the need for a P4P portal to enable explicit communications between P2P and network providers.
First, P2P systems have tremendous flexibility in shaping
their traffic flow. Given that a client interested in a piece of
data can download it from any one of the multiple sites storing the data, there are clear benefits to be had in intelligently
choosing a data source (or, alternately, choosing a peer in
a tit-for-tat system). This flexibility fundamentally changes
the traditional network traffic control problem, which is typically solved in the context of a given traffic demand matrix.
In the updated setting, there are multiple ways of satisfying
the data demands of an application, each resulting in a different traffic demand matrix, and an efficient solution would
require the explicit involvement of the P2P application.
Second, the current network architecture allows only
for limited, implicit communications between network
providers and applications. In this setting, if a P2P application seeks to exploit the flexibility in controlling its data
transfers to improve efficiency, it will have to probe the network to reverse engineer information such as topology, status
and policies. However, this is rather challenging in spite of
significant progress in network measurement techniques. For
one thing, it is clearly redundant and wasteful to have each
application perform probing. Even if this issue is addressed
by a coordinated service for topology inference (e.g., [12]) to
reduce the overhead, the fundamental hurdle is the ability to
perform the inference in an accurate manner. New technologies, such as MPLS, and routers that do not respond to measurement probes make it difficult to infer network characteristics. More importantly, available bandwidth and loss-rate
estimation from end hosts are difficult because their views
are obscured by last-mile bottlenecks; it is difficult for an
end host to identify which links are under-utilized or overutilized. Furthermore, cost and policy information are diffi-
cult, if not impossible, to reverse engineer. For example, it
is difficult for P2P to determine which peers are accessible
through lightly-loaded intradomain links and/or lower-cost
interdomain links (where the cost takes into account factors
such as inter-domain policies, traffic balance ratio between
peering providers, and 95% percentile based billing).
In summary, for traditional applications, routing is made
by network providers using a predictable traffic demand matrix with full network knowledge. With high levels of P2P
traffic, the traffic control problem needs to be jointly solved
by network providers and P2P applications.
2.2 Design Rationale
We consider the following design requirements.
² Better P2P performance. While some P2P systems exploit
locality and network status to have its clients refine their
peerings, the performance improvement is limited due to
factors such as limited network information and slow convergence that is further exacerbated by churn [15]. Using
more accurate network status information, P4P should be
able to identify more efficient connections.
² More efficient network resource usage. By enabling explicit communication between P2P and the network, P4P
can enable applications to use network status information
to reduce backbone traffic and lower operation costs.
² Scalability. P4P should support a large number of users
and P2P networks in very dynamic settings; any proposed
information exchange and optimization techniques should
be computationally inexpensive.
² Privacy preservation. P4P should address a major incentive concern of network providers who may want to preserve privacy when releasing their network information.
² Extensibility. There are many types of P2P applications
with varying features. For instance, P2P systems for file
sharing and streaming might have different needs, such as
P2P streaming having more stringent real-time constraints
than file sharing. Also, some applications use trackers (referred to as appTracker hereafter) to bootstrap and guide
peer selection, while others do not; in addition, peers may
exchange information locally through gossip messages.
P4P should be flexible to handle a wide range of P2P applications with varying requirements and features.
² Incremental deploymentability. We do not target a cleanslate re-design. The P4P framework should be incrementally deployable, one network provider at a time, one P2P
application at a time.
² Provider contribution for P2P acceleration. A networkprovider may have many capabilities which it can provide
to accelerate content distribution for P2P and at the same
time increase its revenue. Examples include class of service, or quality of service that a P2P content provider can
request. Also, a provider may contribute fixed servers as
high-capacity seeds or caches, and this information should
percolate to the P2P application.
2.3 Design Overview
The P4P framework consists of a control-plane component and a data-plane component.
In the control plane, P4P introduces iTrackers to provide
portals for P2P to communicate with network providers. The
introduction of iTrackers allows P4P to divide traffic control
responsibilities between P2P and providers, and also makes
P4P incrementally deployable and extensible.
Specifically, each network provider, be it a conventional
commercial network provider (e.g., AT&T), a university
campus network, or a virtual service provider (e.g., Akamai),
maintains an iTracker for its network. A P2P client obtains
the IP address of the iTracker of its local provider through
DNS query (with a new DNS record type P4P ). Standard
techniques can be applied to allow for multiple iTrackers in
a given domain, especially for fault tolerance and scalability.
An iTracker provides a portal for three kinds of information
regarding the network provider: network status/topology;
provider guidelines/policies; and network capabilities.
In the data plane, P4P allows routers on the data plane to
give fine-grained feedback to P2P and enable more efficient
usage of network resources. Specifically, routers can mark
the ECN bits of TCP packets (or a field in a P2P header),
or explicitly designate flow rates using XCP-like approaches
(e.g., [9]); end hosts then adjust their flow rates accordingly.
For instance, a multihomed network can optimize financial
cost and improve performance through virtual capacity computed based on 95-percentiles [4]. When the virtual capacity
is approached, routers mark TCP packets and end hosts reduce their flow rates accordingly; thus the network provider
can both optimize its cost and performance and allocate more
bandwidth to P2P flows. We emphasize that the data plane
component is optional and can be incrementally deployed.
2.4 P4P Control Plane
In this paper, we focus on the control plane of the P4P
framework. Figure 1 shows the potential entities in the
P4P framework: iTrackers owned by individual network
providers, appTrackers in P2P systems, and P2P clients (or
peers for short). Not all entities might interact in a given
setting. For example, trackerless systems do not have appTrackers. P4P does not dictate the exact information flow,
but rather provides only a common messaging framework,
with control messages encoded in XML for extensibility.
appTracker
peer
iTracker
policy
capability
info
Figure 1. iTracker interfaces and information flow.
iTracker interfaces and functions
The key component of the P4P framework is iTrackers.
iTrackers provide three interfaces that others can query.
The info interface allows others, typically peers inside
the provider network, to obtain network topology and status.
Specifically, given a query for an IP address inside the network, the interface maps the IP address to a (ASID, PID,
LOC) tuple, where ASID is the ID of the network provider
(e.g., its AS number), PID is an opaque ID assigned to a
group of network nodes, and LOC is a virtual or geographical coordinate of the node. Note that the opaque PID is used
to preserve provider privacy at a coarse grain (e.g., a network
provider can assign two PID s to nodes at the same point of
presence or PoP). Note also that LOC can be used to compute
network proximity, which can be helpful in choosing peers.
When sending an info query, a peer may optionally include
its swarm ID (e.g., info hash of a torrent). The iTracker may
keep track of peers participating in a swarm.
The policy interface allows others, for example peers
or appTrackers, to obtain policies and guidelines of the network. Policies specify how a network provider would like its
networks to be utilized at a high level, typically regardless of
P2P applications; while guidelines are specific suggestions
for P2P to use the network resources. To name a few examples of network policies: (1) traffic ratio balance policy,
defining the ratio between inbound and outbound traffic volumes, for interdomain peering links; (2) coarse-grain timeof-day link usage policy, defining the desired usage pattern
of specific links (e.g., avoid using links that are congested
during peak times); and (3) fine-grain link usage policy. An
example of network guidelines is that a network provider
computes peering relationships for clusters of peers (e.g.,
clustered by PID ). The policy interface can also return a set
of normalized inter-PID costs, which indicate costs incurred
to the provider when peers in two PIDs communicate.
The capability interface allows others, for example
peers or content providers (through appTrackers), to request
network providers’ capabilities. For example, a network
provider may provide different classes of services or ondemand servers in its network. Then an appTracker may
ask iTrackers in popular domains to provide such servers and
then use them as peers to accelerate P2P content distribution.
A network provider may choose to implement a subset
of the interfaces. The richness of information conveyed is
also determined by the network provider. Note that a network provider may also enforce some access control to the
interfaces to preserve security and privacy.
Examples
Now we give two examples to illustrate how the iTracker interfaces are utilized. Figure 2 shows an example P2P application with an appTracker using the info and policy
interfaces to request network topology/status and guidelines/policies information. In the example, a P2P system spans two network providers A and B. Each network
provider runs an iTracker for its own network. Peers a and
b query their local iTrackers through the info and policy
interfaces when bootstrapping; they then register with the
appTracker and forward it the information obtained fromFigure 2. An example of P2P obtaining network topology/status and policies/guidelines from portal iTrackers.
Figure 3. An example of P2P accessing network capability through iTrackers.
iTrackers. The appTracker makes peer selection considering both application requirements and iTracker information.
Note that as a variant, it might be that instead of the peers
query the iTrackers, the appTracker, trusted by a network
provider (e.g., a major P2P developer), queries the iTrackers
to reduce information exposure to general peers.
Figure 3 shows another example of using P4P. It shows
how to request network capabilities through the capability
interface. Specifically, the appTracker sends a request to
iTracker B asking the network provider to allocate fixed,
high-capacity servers to aid in distributing content. The
iTracker allocates a server in its network and returns its address to the appTracker. The appTracker will then include
the server in returned peer sets for those peers in B.
3 Evaluations
P4P’s effectiveness depends on what information is communicated through the portal and what algorithms are used
for controlling traffic. We have evaluated the effectiveness of
P4P using both simulations and real Internet experiments on
PlanetLab. Our results show that P4P can improve not only
P2P application performance but also network provider effi-
ciency. Due to space limitations, we report on only a small
fraction of our evaluations.
3.1 Optimization Methodology
We report our evaluations on how a network provider and
peers can effectively utilize the policy interface. Specifi-
cally, we consider an optimization that minimizes intradomain traffic by taking into account swarm characteristics and
current levels of background traffic. we consider the following setup. There are K swarms in a provider’s network. Each
peer of a swarm obtains a unique swarm ID from the corresponding appTracker of the swarm, and reports it to the
iTracker. The iTracker keeps track of the peers in a given
swarm, including the number of peers at the same PoP, and
the upstream and downstream link capacity of each peer. We
refer to peers at PoP i as PoP-i peers. The iTracker then
computes the peering guidelines based on swarm statistics
and network status.
Specifically, the iTracker constructs an abstract topology
G = (V;E) with nodes representing PoPs and edges interPoP links. The iTracker collects network status information including (1) be, the amount of background traffic on
edge e, (2) ce, the capacity of edge e, and (3) Ie(i; j), the
indicator of edge e being on the route from node i to j in
G. The iTracker also computes u˜
k
i
and d˜k
i
, the aggregated
uploading and downloading capacity of all PoP-i peers of
the k-th swarm in the network, respectively. Note that the
iTracker can compute u˜
k
i
and d˜k
i
using the information that
it obtains from the P2P clients and from its knowledge of
the access link capacities (both downstream and upstream)
for each peer. The iTracker takes into account any userimposed bandwidth limitations and aggregates them together
to obtain u
k
i
and d
k
i
for each PoP i. The iTracker then computes u
k
i = maxfu˜
k
i ¡d˜k
i
; 0g, and d
k
i = maxfd˜k
i ¡u˜
k
i
; 0g. Thus
u
k
i
and d
k
i
are the remaining uploading (supply) and downloading (demand) capacity that can be used to interface with
peers in other PoPs.
The iTracker (periodically) solves a bi-level optimization
problem to compute the peering guidelines: balancing the
traffic (i.e., minimizing the maximum link utilization) while
maximizing the overall throughput for each swarm:
min max
e2E
be + ak ai aj=6 i
t
k
i j
Ie(i; j)=ce
s:t: 8k;maxai aj=6 i
t
k
i j
;
s:t:8 PoP i;aj=6 i
t
k
i j · u
k
i
;
8 PoP i;aj=6 i
t
k
ji · d
k
i
;
8i =6 j;t
k
i j ¸ 0;
where t
k
i j
is the amount of traffic PoP-i peers upload to PoP- j
peers in the k-th swarm. We solve this optimization problem as follows. We first solve the second-level problem to
obtain the optimal throughput T
k
opt
for each swarm k. This
enables us to transform the k-th second-level problem into a
constraint: ai aj=6 i
t
k
i j = T
k
opt
. Thus, we can solve the original problem. The iTracker derives the peering guidelines,
which is a set of normalized weights w
k
i j = t
k
i j
=aj
t
k
i j
for PoP
i and j. A PoP-i peer would pick a PoP- j peer with probability w
k
i j
. Next, the iTracker maps PoPs to anonymous PIDs
and the computed weights to corresponding inter-PID peering probability values. The appTracker can obtain the interPID probability values and use it for peer selection.
3.2 Results
We have evaluated the effectiveness of P4P using both
simulations and real Internet experiments on PlanetLab. For
the simulation, we built a realistic BitTorrent simulator. For
PlanetLab experiments, we use Liveswarms [14]. We chose
these two P2P systems as one is file sharing while the other is
streaming. Below, we report some of our evaluation results.
We first show the results on P4P-enabled BitTorrent,
where the appTracker adopts iTracker suggested guidelines
along with a small fraction of random inter-PoP peers for
robustness. We use Abilene and the PoP-level topology of
AT&T with all link capacities at 10Gbps. In the evaluations, 50
100
150
200
250
300
350
400
450
95% Completion time (second)
100 200 300 400 500 600 700 800
total # of peers
native P2P adaptation
P4P
(a) Completion time.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
50 100 150 200 250 300 350
Cumulative percentage
Completion time (second)
P4P
native P2P adaptation
(b) CDF completion time (700 peers).
0
2
4
6
8
10
12
14
16
0 50 100 150 200 250 300 350
Link utilization (%)
simulation time (second)
native P2P adaptation
P4P
(c) Utilization of link ATLA-IPLS (700 peers).
Figure 4. Results on integrating P4P with BitTorrent on Abilene.
50
100
150
200
250
300
350
400
450
95% Completion time (second)
400 500 600 700 800 900 1000
total # of peers
native P2P adaptation
P4P
(a) Completion time.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
50 100 150 200 250 300 350
Cumulative percentage
Completion time (second)
P4P
native P2P adaptation
(b) CDF completion time (700 peers).
0
1
2
3
4
5
6
0 50 100 150 200 250 300 350
Link utilization (%)
simulation time (second)
native P2P adaptation
P4P
(c) Utilization of link ATLA-IPLS (700 peers).
Figure 5. Results on integrating P4P with BitTorrent on AT&T PoP topology.
we connect each peer to a random PoP through an access
link. The capacities of access links follow the distribution
used in [1]. We evaluate two equal-sized swarms each sharing a 256MB file, with block size being 256KB. Initially
each swarm has only one seed with 1Gbps upload capacity.
Figures 4 and 5 plot the completion time, cumulative
completion time and link utilization for Abilene and AT&T,
respectively, with a varying number of peers. We make the
following observations. First, P4P improves P2P completion time by approximately 45%. Second, P4P improves
the link utilization by approximately 50% and 70% in Abilene and AT&T, respectively, when compared with the native
P2P adaptation. Further, P4P also reduces the duration of
high traffic load by approximately a half, as peers finish their
downloads faster. Thus P4P can reduce the intensity of P2P
traffic load on the underlying network dramatically.
Next we report the evaluation results on integrating P4P
with liveswarms. Liveswarms is a P2P-based application that
adapts BitTorrent to video streaming. We conduct experiments on real Abilene network, using 53 PlanetLab nodes,
to stream a large video file. Each experiment lasts 900 seconds. We log the amount of data exchanged between nodes,
and compute the load on each Abilene backbone link using
OSPF routing with Abilene’s IGP link weights. The total
amount of traffic on each Abilene backbone link is plot in
Figure 6. We find that liveswarms achieves approximately
the same throughput when integrated with P4P. However, the
average link load is 1.1Gbps when native P2P adaptation is
used, while the load reduces to 0.37Gbps when liveswarms is
integrated with P4P. Thus P4P results in approximately 66%
reduction on average link utilization.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Traffic on links (Gbit)
Abilene links
native P2P adaptation
P4P
Figure 6. Traffic volumes when integrating P4P with a
video streaming application.
Summary: P4P can improve both completion time and link
utilization by approximately 50-70% for BitTorrent to share
files, compared with the native P2P adaptation, on Abilene
and AT&T networks. P4P achieve similar benefits in real
experiments using liveswarms to stream video on real Abilene network. Further, P4P is robust to heterogeneity in the
networks where links have different capacities.
4 Discussions
Q: [P2P Self Adaptation] Why cannot a P2P system
achieve the benefits of P4P by itself? Or why do we need
explicit communications between P2P and providers?
A: As we described in Section 1, it is difficult for applications to reverse engineer network status. It is even more
difficult to infer provider routing policies. Although some
P2P applications can use locality-based heuristics, there can
be problems with this approach. For example, in a wireless
network where P2P nodes communicate through a base station, peering using local peers sharing the same base stationmay require more wireless bandwidth than through the base
station to other non-local peers. As another example, a common issue exists in UK is that network providers buy their
DSL “last mile” connectivity via a BT central pipe. More
specifically, BT owns all of the exchange equipment and
connectivity between a DSL customer and a central hand-off
location. The connectivity from a DSL customer to its network provider is first routed through BT to a physical handoff point. The hand-off point between BT and the network
provider is what BT terms a BT central pipe. This connection can be many orders of magnitude more expensive than
IP transit. Thus, it can be much more expensive for a network provider to have a customer retrieve a file from another
customer on its network, than it is to go off the network for
the file. Also, iTrackers provide a natural interface to access
provider capabilities.
Q: [P2P incentives] It is clear that network providers can
benefit from P4P. What are the incentives for P2P to participate in the P4P framework?
A: There are potentials for both network providers and P2P
to benefit from adopting P4P, as we clearly demonstrated in
our evaluations. In another set of experiments not shown
here, we have shown that through P4P, a multihomed system (e.g., a university campus network) may allocate much
more bandwidth to P2P without increasing its financial cost
under a typical charging model. Furthermore, the P4P framework leaves much flexibility for P2P (e.g., P2P can integrate
provider suggestions with its local application-specific requirements). On the other hand, without P4P, the providers
may impose rate limiting to control their financial cost.
Overall, many P2P developers are recognizing that as P2P
consumes a significant portion of network resources without
generating much revenue for providers, there is a real possibility that network providers may limit P2P usage to reduce
cost. Thus, effective cooperation through the P4P framework
can be attractive.
Q: [Scalable Implementation] There can be a large number of P2P networks in a provider’s network. How can it be
feasible for the iTracker to handle the load associated with
orchestrating all these networks?
A: Scalability could be addressed using several techniques.
First, we need to consider optimizing only the heavyhitters, namely those P2P networks that comprise of a large
number of peers and generate a substantial amount of traf-
fic. If a small number of P2P networks account for a large
fraction of traffic, then the provider can focus its attention on
those P2P networks. In order to quantify to what extent this
phenomenon appears in practice, we analyzed the instantaneous swarm behavior of every movie torrent published by
thepiratebay.org , a popular portal for BitTorrent content.
In total, we analyzed 34,721 swarms to determine the number of leechers and the size of data being requested by the
leechers. We find that only 0.72% of swarms had an excess of hundred leechers, and that the bulk of the instantaneous demand for content is from a small number of swarms,
specifically, 1.22% of the swarms are responsible for about
50% of the demand. Analyzing and optimizing a small fraction of swarms should mostly suffice.
Second, we could replicate the iTracker functionality and
further organize the iTrackers into a two-level hierarchy. The
top-level server aggregates information from multiple P2P
systems, solves the optimization problem, and distributes allocation decisions. The bottom-level servers are the ones
contacted by the clients and are tasked with performing other
operations, such as finding local connections. We just need
to ensure that all peers within the same P2P network are directed to the same second-level server, and this could be done
using a consistent hashing scheme [8].
Q: [Robustness] A major issue in P2P is to provide robustness. For instance, a BitTorrent client maintains a pool of
randomly selected neighbors with which it is just exchanging meta-data information. Do the locality-aware P4P techniques reduce robustness?
A: P4P does not limit the mechanisms for improving robustness. In the basic operation mode, the iTrackers provide
only hints, and an appTracker can always select a certain
number of random connections to ensure diversity for robustness. This typically will not substantially increase the
provider cost. A related robustness feature is that iTrackers
are not on the critical path. Thus, if iTrackers are down, P2P
applications can still make default application decisions.
5 Conclusion and Future Work
We presented P4P, a simple and flexible framework
to enable explicit cooperation between P2P and network
providers. Our evaluations demonstrate that it can be a
promising approach to improve both application performance and provider efficiency. There are many avenues for
further study. In particular, it is important to evaluate the
framework, study what information should be communicated
through the portal, identify what algorithms and techniques
could be jointly employed by providers and P2P to improve
efficient network traffic control, and quantify its benefits in
large-scale, realistic networks.
6 Acknowledgments
Haiyong Xie and Y. Richard Yang were supported in
part by grants from the U.S. NSF. Charles Kalmanek, Doug
Pasko, Laird Popkin, Hao Wang, and Ye Wang have given us
very valuable suggestions.
7 References
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[2] Cisco. Network-based application recognition
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[3] F5 White Paper. Bandwidth management for peer-topeer applications. http://www.f5.com/solutions/
technology/rateshaping_wp.html , Jan. 2006.
[4] D. Goldenberg, L. Qiu, H. Xie, Y. R. Yang, and
Y. Zhang. Optimizing cost and performance for internet multihoming. In Proceedings of ACM SIGCOMM
’04, Portland, OR, August 2004.[5] S. Guha, N. Daswani, and R. Jain. An experimental
study of the skype peer-to-peer VoIP system. In Proc
of IPTPS, Santa Barbara, CA, Feb. 2006.
[6] K. Gummadi, R. Dunn, S. Saroiu, S. Gribble, H. Levy,
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SOSP ’03, Bolton Landing, Oct. 2003.
[7] T. Karagiannis, P. Rodriguez, and K. Papagiannaki.
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[8] D. Karger, E. Lehman, T. Leighton, M. Levine,
D. Lewin, and R. Panigrahy. Consistent hashing and
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[9] D. Katabi, M. Handley, and C. Rohrs. Congestion control for high bandwidth-delay product networks. In
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[10] R. Keralapura, N. Taft, C.-N. Chuah, and G. Iannaccone. Can ISPs take the heat from overlay networks?
In Proc. of HotNets-III, San Diego, CA, Nov. 2004.
[11] Lightreading.com. P2P plagues service providers.
[12] H. V. Madhyastha, T. Isdal, M. Piatek, C. Dixon,
T. Anderson, A. Krishnamurthy, and A. Venkataramani. iPlane: An information plane for distributed services. In Proc. of OSDI, Seattle, WA, 2006.
[13] A. Parker. The true picture of peer-to-peer filesharing.
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[14] M. Piatek, C. Dixon, A. Krishnamurthy, and T. Anderson. Liveswarms: Adapting bittorrent for end host multicast. Technical Report UW-CSE-06-11-01, 2006.
[15] M. Piatek, T. Isdal, T. Anderson, A. Krishnamurthy,
and A. Venkataramani. Do Incentives Build Robustness in BitTorrent? In Proc. of NSDI, 2007.
[16] L. Qiu, Y. R. Yang, Y. Zhang, and S. Shenker. Selfish
routing in Internet-like environments. In Proc of SIGCOMM, Karlsruhe, Germany, Aug. 2003.
[17] S. Seetharaman and M. Ammar. Characterizing and
mitigating inter-domain policy violations in overlay
routes. In Proc of ICNP, 2006.
[18] G. Shen, Y. Wang, Y. Xiong, B. Y. Zhao, and Z.-L.
Zhang. HPTP: Relieving the tension between ISPs and
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p2p traffic. In Proc of GP2P, Chicago, IL, Apr. 2004.
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IPTPS, Bellevue, WA, Feb. 2007.
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