RFC5632: Comcast's ISP Experiences in a Proactive Network Provider Participation for P2P (P4P) Technical Trial
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Related keywords:
(DCIA)
(Distributed Computing Industry Association)
(Internet Service Provider)
(ISP)
(P2P)
(P4P)
(peer-to-peer)
(Proactive Network Provider Partication for P2P)
Network Working Group C. Griffiths
Request for Comments: 5632 J. Livingood
Category: Informational Comcast
L. Popkin
Pando
R. Woundy
Comcast
Y. Yang
Yale
September 2009
Comcast's ISP Experiences in a Proactive Network Provider Participation
for P2P (P4P) Technical Trial
Abstract
This document describes the experiences of Comcast, a large cable
broadband Internet Service Provider (ISP) in the U.S., in a Proactive
Network Provider Participation for P2P (P4P) technical trial in July
2008. This trial used P4P iTracker technology, which is being
considered by the IETF as part of the Application Layer Transport
Optimization (ALTO) working group.
Status of This Memo
This memo provides information for the Internet community. It does
not specify an Internet standard of any kind. Distribution of this
memo is unlimited.
Copyright Notice
Copyright (c) 2009 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents in effect on the date of
publication of this document (http://trustee.ietf.org/license-info).
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. High-Level Details . . . . . . . . . . . . . . . . . . . . . . 3
3. Differences between the P4P iTrackers Used . . . . . . . . . . 4
3.1. P4P Fine Grain . . . . . . . . . . . . . . . . . . . . . . 4
3.2. P4P Coarse Grain . . . . . . . . . . . . . . . . . . . . . 5
3.3. P4P Generic Weighted . . . . . . . . . . . . . . . . . . . 5
4. High-Level Trial Results . . . . . . . . . . . . . . . . . . . 5
4.1. Swarm Size . . . . . . . . . . . . . . . . . . . . . . . . 6
4.2. Impact on Download Speed . . . . . . . . . . . . . . . . . 7
4.3. General Impacts on Upstream and Downstream Traffic and
Other Interesting Data . . . . . . . . . . . . . . . . . . 7
5. Important Notes on Data Collected . . . . . . . . . . . . . . 8
6. Next Steps . . . . . . . . . . . . . . . . . . . . . . . . . . 9
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 10
9. Informative References . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
Comcast is a large broadband Internet Service Provider (ISP), based
in the U.S., serving the majority of its customers via cable modem
technology. A trial was conducted in July 2008 with Pando Networks,
Yale, and several ISP members of the P4P working group, which is part
of the Distributed Computing Industry Association (DCIA). Comcast is
a member of the DCIA's P4P Working Group, whose mission is to work
with Internet Service Providers (ISPs), peer-to-peer (P2P) companies,
and technology researchers to develop "P4P" mechanisms, such as so-
called "iTrackers" (hereafter P4P iTrackers), that accelerate
distribution of content and optimize utilization of ISP network
resources. P4P iTrackers theoretically allow P2P networks to
optimize traffic within each ISP, reducing the volume of data
traversing the ISP's infrastructure and creating a more manageable
flow of data. P4P iTrackers can also accelerate P2P downloads for
end users.
P4P's iTracker technology [SIGCOMM] was conceptually discussed with
the IETF at the Peer-to-Peer Infrastructure (P2Pi) Workshop held on
May 28, 2008, at the Massachusetts Institute of Technology (MIT), as
documented in [RFC5594]. This work was discussed in greater detail
at the 72nd meeting of the IETF, in Dublin, Ireland, in the ALTO BoF
(Birds of a Feather meeting) on July 29, 2008. Due to interest from
the community, Comcast shared P4P iTracker trial data at the 73rd
meeting of the IETF, in Minneapolis, Minnesota, in the ALTO BoF on
November 18, 2008. Since that time, discussion of P4P iTrackers and
alternative technologies has continued among participants of the ALTO
working group.
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The P4P iTracker trial was conducted, in cooperation with Pando,
Yale, and three other P4P member ISPs, from July 2 to July 17, 2008.
This was the first P4P iTracker trial over a cable broadband network.
The trial used a Pando P2P client, and Pando distributed a special
21-MB licensed video file in order to measure the effectiveness of
P4P iTrackers. A primary objective of the trial was to measure the
effects that increasing the localization of P2P swarms would have on
P2P uploads, P2P downloads, and ISP networks, in comparison to normal
P2P activity.
2. High-Level Details
As noted in Section 1 of [DynamicSwarmMgmt], a swarm is defined in
the following way:
The content and the set of peers distributing it [a file] is
usually called a torrent. A peer that only uploads content is
called a seed, while a peer that uploads and downloads at the same
time is called a leecher. The connected set of peers
participating in the piece exchanges of a torrent is referred to
as a swarm.
There were five different swarms for the content used in the trial.
The second, third, and fourth used different P4P iTrackers: Generic,
Coarse Grained, and Fine Grained, all of which are described in
Section 3. The fifth was a proprietary Pando mechanism. (The
results of the fifth swarm, while satisfactory, are not included here
since our focus is on open standards and a mechanism that may be
leveraged for the benefit of the entire community of P2P clients.)
Comcast deployed a P4P iTracker server in its production network to
support this trial, and configured multiple iTracker files to provide
varying levels of localization to clients.
In the trial itself, a P2P client begins a P2P session by querying a
pTracker, which runs and manages the P2P network. The pTracker
occasionally queries the P4P iTracker, which in this case was
maintained by Comcast, the ISP. Other ISPs either managed their own
P4P iTracker or used Pando or Yale to host their P4P iTracker files.
The P4P iTracker returns network topology information to the
pTracker, which then communicates with P2P clients, in order to
enable P2P clients to make network-aware decisions regarding peers.
The Pando client was enabled to capture extended logging, when the
version of the client included support for it. The extended logging
included the source and destination IP address of all P2P transfers,
the number of bytes transferred, and the start and end timestamps.
This information gives a precise measurement of the data flow in the
network, allowing computation of data transfer volumes as well as
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data flow rates at each point in time. With standard logging, Pando
captured the start and completion times of every download, as well as
the average transfer rate observed by the client for the download.
Pando served the data from an origin server external to Comcast's
network. This server served about 10 copies of the file, after which
all transfers (about 1 million downloads across all ISPs) were
performed purely via P2P.
The P2P clients in the trial start with tracker-provided peers, then
use peer exchange to discover additional peers. Thus, the initial
peers were provided according to P4P iTracker guidance (90% guidance
based on P4P iTracker topology and 10% random guidance), then later
peers discover the entire swarm via either additional announces or
peer exchange.
3. Differences between the P4P iTrackers Used
Given the size of the Comcast network, it was felt that in order to
truly evaluate the P4P iTracker application we would need to test
various network topologies that reflected its network and would help
gauge the level of effort and design requirements necessary to get
correct statistical data out of the trial. In all cases, P4P
iTrackers were configured with automation in mind, so that any
successful P4P iTracker configuration would be automatically
updating, rather than manually configured on an ongoing basis. All
P4P iTrackers were hosted on the same small server, and it appeared
to be relatively easy and inexpensive to scale up a P4P iTracker
infrastructure should P4P iTracker-like mechanisms become
standardized and widely adopted.
3.1. P4P Fine Grain
The Fine Grain topology was the first and most complex P4P iTracker
that we built for this trial. It was a detailed mapping of Comcast
backbone-connected network Autonomous System Numbers (ASNs) to IP
Aggregates, which were weighted based on priority and distance from
each other. Included in this design was a prioritization of all Peer
and Internet transit connected ASNs to the Comcast backbone to ensure
that P4P traffic would prefer settlement-free and lower-cost networks
first, and then more expensive transit links. This attempted to
optimize and lower transit costs associated with this traffic. We
then took the additional step of detailing each ASN and IP Aggregate
into IP subnets down to Optical Transport Nodes (OTNs) where all
Cable Modem Termination Systems (CMTS, as briefly defined in Section
2.6 of [RFC3083]) reside . This design gave a highly localized and
detailed description of the Comcast network for the iTracker to
disseminate. This design defined 1,182 P4P iTracker node
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identifiers, and resulted in a 107,357-line configuration file.
This P4P iTracker was obviously the most time-consuming to create and
the most complex to maintain. Trial results indicated that this
level of localization was too high, and was less effective compared
to lower levels of localization.
3.2. P4P Coarse Grain
Given the level of detail in the Fine Grain design, it was important
that we also enable a high-level design, which still used priority
and weighting mechanisms for the Comcast backbone and transit links.
The Coarse Grain design was a limited or summarized version of the
Fine Grain design, which used the ASN to IP Aggregate and weighted
data for transit links, but removed all additional localization data.
This ensured we would get similar data sets from the Fine Grain
design, but without the more detailed localization of each of the
networks attached to the Comcast backbone. This design defined 22
P4P iTracker node identifiers, and resulted in a 998-line
configuration file.
From an overall cost, complexity, risk, and effectiveness standpoint,
this was judged to be the optimal P4P iTracker for Comcast.
Importantly, this did not require revealing the complex, internal
network topology that the Fine Grain did. Updates to this iTracker
were also far simpler to automate, which will better ensure that it
is accurate over time, and keeps administrative overhead relatively
low. However, the differences, costs, and benefits of Coarse Grain
and Generic Weighted (see below) likely merit further study.
3.3. P4P Generic Weighted
The Generic Weighted design was a copy of the Coarse Grained design,
but instead of using ISP-designated priority and weights, all weights
were defaulted to pre-determined parameters that the Yale team had
designed. All other data was replicated from the Coarse Grain
design. Gathering and providing the information necessary to support
the Generic Weighted iTracker was roughly the same level of effort as
for Coarse Grain.
4. High-Level Trial Results
Trial data was collected by Pando Networks and Yale University, and
raw trial results were shared with Comcast and all of the other ISPs
involved in the trial. Analysis of the raw results was performed by
Pando and Yale, and these organizations delivered an analysis of the
P4P iTracker trial. Using the raw data, Comcast also analyzed the
trial results. Furthermore, the raw trial results for Comcast were
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shared with Net Forecast, Inc., which performed an independent
analysis of the trial for Comcast.
4.1. Swarm Size
During the trial, downloads peaked at 24,728 per day, per swarm, or
nearly 124,000 per day for all five swarms. The swarm size peaked at
11,703 peers per swarm, or nearly 57,000 peers for all five swarms.
We observed a comparable number of downloads in each of the five
swarms.
For each swarm, Table 1 below gives the number of downloads per swarm
from Comcast that finished downloading, and the number of downloads
from Comcast that canceled downloading before finishing.
Characteristics of P4P iTracker Swarms:
+-----------+-----------+---------------+------------+--------------+
| Swarm | Completed | Cancellations | Total | Cancellation |
| | Downloads | | Attempts | Rate |
+-----------+-----------+---------------+------------+--------------+
| Random | 2,719 | 89 | 2,808 | 3.17% |
| (Control) | | | | |
| --------- | --------- | ----------- | ---------- | ----------- |
| P4P Fine | 2,846 | 64 | 2,910 | 2.20% |
| Grained | | | | |
| --------- | --------- | ----------- | ---------- | ----------- |
| P4P | 2,775 | 63 | 2,838 | 2.22% |
| Generic | | | | |
| Weight | | | | |
| --------- | --------- | ----------- | ---------- | ----------- |
| P4P | 2,886 | 52 | 2,938 | 1.77% |
| Coarse | | | | |
| Grained | | | | |
+-----------+-----------+---------------+------------+--------------+
Table 1: Per-Swarm Size and Cancellation Rates
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4.2. Impact on Download Speed
The results of the trial indicated that P4P iTrackers can improve the
speed of downloads to P2P clients. In addition, P4P iTrackers were
effective in localizing P2P traffic within the Comcast network.
Impact of P4P iTrackers on Downloads:
+--------------+------------+------------+-------------+------------+
| Swarm | Global Avg | Change | Comcast Avg | Change |
| | bps | | bps | |
+--------------+------------+------------+-------------+------------+
| Random | 144,045 | n/a | 254,671 bps | n/a |
| (Control) | bps | | | |
| ---------- | ---------- | ---------- | ---------- | ---------- |
| P4P Fine | 162,344 | +13% | 402,043 bps | +57% |
| Grained | bps | | | |
| ---------- | ---------- | ---------- | ---------- | ---------- |
| P4P Generic | 163,205 | +13% | 463,782 bps | +82% |
| Weight | bps | | | |
| ---------- | ---------- | ---------- | ---------- | ---------- |
| P4P Coarse | 166,273 | +15% | 471,218 bps | +85% |
| Grained | bps | | | |
+--------------+------------+------------+-------------+------------+
Table 2: Per-Swarm Global and Comcast Download Speeds
4.3. General Impacts on Upstream and Downstream Traffic and Other
Interesting Data
An analysis of the effects of P4P iTracker use on upstream
utilization and Internet transit was also interesting. It did not
appear that P4P iTrackers significantly increased upstream
utilization in the Comcast access network; in essence, uploading was
already occurring no matter what and a P4P iTracker in and of itself
did not appear to materially increase uploading for this specific,
licensed content. (A P4P iTracker is not intended as a solution for
the potential of network congestion to occur.) Random was 143,236 MB
and P4P Generic Weight was 143,143 MB, while P4P Coarse Grained was
139,669 MB. We also observed that using a P4P iTracker reduced
outgoing Internet traffic by an average of 34% at peering points.
Random was 134,219 MB and P4P Generic Weight was 91,979 MB, while P4P
Coarse Grained was 86,652 MB.
In terms of downstream utilization, we observed that the use of a P4P
iTracker reduced incoming Internet traffic by an average of 80% at
peering points. Random was 47,013 MB, P4P Generic Weight was 8,610
MB, and P4P Coarse Grained was 7,764 MB. However, we did notice that
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download activity in the Comcast access network increased somewhat,
from 56,030 MB for Random, to 59,765 MB for P4P Generic Weight, and
60,781 MB for P4P Coarse Grained. Note that for each swarm, the
number of downloaded bytes according to logging reports is very close
to the number of downloads multiplied by file size. But they do not
exactly match due to log report errors and duplicated chunks. One
factor contributing to the differences in access network download
activity is that different swarms have different numbers of
downloaders, due to random variations during uniform random
assignment of downloaders to swarms (see Table 1). One interesting
observation is that Random has higher cancellation rate (3.17%) than
that of the guided swarms (1.77%-2.22%). Whether guided swarms
achieve lower cancellation rate is an interesting issue for future
research.
5. Important Notes on Data Collected
Raw data is presented in this document. We did not normalize traffic
volume data (e.g., upload and download) by the number of downloads in
order to preserve this underlying raw data.
We also recommend that readers not focus too much on the absolute
numbers, such as bytes downloaded from internal sources and bytes
downloaded from external sources. Instead, we recommend readers
focus on ratios such as the percentage of bytes downloaded that came
from internal sources in each swarm. As a result, the small random
variation between number of downloads of each swarm does not distract
readers from important metrics like shifting traffic from external to
internal sources, among other things.
We also wish to note that the data was collected from a sample of the
total swarm. Specifically, there were some peers running older
versions of the Pando client that did not implement the extended
transfer logging. For those nodes, which participated in the swarms
but did not report their data transfers, we have download counts.
The result of this is that, for example, the download counts
generated from the standard logging are a bit higher than the
download counts generated by the extended logging. That being said,
over 90% of downloads were by peers running the newer software, which
we believe shows that the transfer records are highly representative
of the total data flow.
In terms of which analysis was performed from the standard logging
compared to extended logging, all of the data flow analysis was
performed using the extended logging. Pando's download counts and
performance numbers were generated via standard logging (i.e., all
peers report download complete/cancel, data volumes, and measured
download speed on the client). Yale's download counts and
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performance numbers were derived via extended logging (e.g., by
summing the transfer records, counting IP addresses reported, etc.).
One benefit of having two data sources is that we can compare the
two. In this case, the two approaches both reported comparable
impacts.
6. Next Steps
One objective of this document is to share with the IETF community
the results of one P4P iTracker trial in a large broadband network,
given skepticism regarding the benefits to P2P users as well as to
ISPs. From the perspective of P2P users, P4P iTrackers potentially
deliver faster P2P downloads. At the same time, ISPs can increase
the localization of swarms, enabling them to reduce bytes flowing
over transit points, while also delivering an optimized P2P
experience to customers. However, an internal analysis of varying
levels of P4P iTracker adoption by ISPs leads us to believe that,
while P4P iTracker-type mechanisms are valuable on a single ISP
basis, the value of P4P iTrackers increases dramatically as many ISPs
choose to deploy it.
We believe these results can inform the technical discussion in the
IETF over how to use P4P iTracker mechanisms. Should such a
mechanism be standardized, the use of ISP-provided P4P iTrackers
should probably be an opt-in feature for P2P users, or at least a
feature of which they are explicitly aware of and which has been
enabled by default in a particular P2P client. In this way, P2P
users could choose to opt-in either explicitly or by their choice of
P2P client in order to choose to use the P4P iTracker to improve
performance, which benefits both the user and the ISP at the same
time. Importantly in terms of privacy, the P4P iTracker makes
available only network topology information, and would not in its
current form enable an ISP, via the P4P iTracker, to determine which
P2P clients were downloading any specific content, whether to
determine, for example, if content was a song or a movie or even the
title.
It is also possible that a P4P iTracker type of mechanism, in
combination with a P2P cache, could further improve P2P download
performance, which merits further study. In addition, this was a
limited trial that, while very promising, indicates a need for
additional technical investigation and trial work. Such a follow-up
study should explore the effects of P4P iTrackers when more P2P
client software variants are involved, with larger swarms, and with
additional and more technically diverse content (file size, file
type, duration of content, etc.).
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7. Security Considerations
This document does not propose any kind of protocol, practice or
standard.
The experiment did show that an ISP can improve performance without
exposing fine-grained details about network structure, which might
otherwise be a security concern (see Section 3.1 (P4P Fine Grain) and
Section 3.2 (P4P Coarse Grain). Section 6 (Next Steps) mentions that
the opt-in architecture allows P2P users to maintain privacy.
Other security aspects were not considered in the experiment, which
focused on performance measurements.
8. Acknowledgements
The authors wish to acknowledge the hard work of all of the P4P
working group members, and specifically the focused efforts of the
teams at both Pando and Yale for the trial itself. Finally, the
authors recognize and appreciate Peter Sevcik and John Bartlett of
NetForecast, Inc., for their valued independent analysis of the trial
results.
9. Informative References
[DynamicSwarmMgmt]
Carlsson, N. and G. Dan, "Dynamic Swarm Management for
Improved BitTorrent Performance", USENIX 8th International
Workshop on Peer-to-Peer Systems, March 2009,
<http://www.usenix.org/events/iptps09/tech/full_papers/
dan/dan_html/>.
[RFC3083] Woundy, R., "Baseline Privacy Interface Management
Information Base for DOCSIS Compliant Cable Modems and
Cable Modem Termination Systems", RFC 3083, March 2001.
[RFC5594] Peterson, J. and A. Cooper, "Report from the IETF Workshop
on Peer-to-Peer (P2P) Infrastructure, May 28, 2008",
RFC 5594, July 2009.
[SIGCOMM] Xie, H., Yang, Y., Krishnamurthy, A., Liu, Y., and A.
Silberschatz, "ACM SIGCOMM 2008 - P4P: Provider Portal for
Applications", Association for Computing Machinery SIGCOMM
2008 Proceedings, August 2008,
<http://ccr.sigcomm.org/online/files/p351-xieA.pdf>.
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Authors' Addresses
Chris Griffiths
Comcast Cable Communications
One Comcast Center
1701 John F. Kennedy Boulevard
Philadelphia, PA 19103
US
EMail: chris_griffiths@cable.comcast.com
URI: http://www.comcast.com
Jason Livingood
Comcast Cable Communications
One Comcast Center
1701 John F. Kennedy Boulevard
Philadelphia, PA 19103
US
EMail: jason_livingood@cable.comcast.com
URI: http://www.comcast.com
Laird Popkin
Pando Networks
520 Broadway Street
10th Floor
New York, NY 10012
US
EMail: laird@pando.com
URI: http://www.pando.com
Richard Woundy
Comcast Cable Communications
27 Industrial Avenue
Chelmsford, MA 01824
US
EMail: richard_woundy@cable.comcast.com
URI: http://www.comcast.com
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Richard Yang
Yale University
51 Prospect Street
New Haven, CT 06520
US
EMail: yry@cs.yale.edu
URI: http://www.cs.yale.edu
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