Helping Users Shop for ISPs with Internet Nutrition Labels
W. Keith Edwards
CNRS/UPMC Sorbonne Univ.
Rebecca E. Grinter
Walter de Donato
University of Napoli Federico II
When purchasing home broadband access from Internet service
providers (ISPs), users must decide which service plans are most
appropriate for their needs. Today, ISPs advertise their available
service plans using only generic upload and download speeds. Un-
fortunately, these metrics do not always accurately reflect the vary-
ing performance that home users will experience for a wide range
of applications. In this paper, we propose that each ISP service plan
carry a “nutrition label” that conveys more comprehensive informa-
tion about network metrics along many dimensions, including vari-
ous aspects of throughput, latency, loss rate, and jitter. We first jus-
tify why these metrics should form the basis of a network nutrition
label. Then, we demonstrate that current plans that are superficially
similar with respect to advertised download rates may have differ-
ent performance according to the label metrics. We close with a
discussion of the challenges involved in presenting a nutrition label
to users in a way that is both accurate and easy to understand.
Categories and Subject Descriptors
C.2.3 [Computer-Communication Networks]: Network Opera-
tions—Network Management; C.2.3 [Computer-Communication
Networks]: Network Operations—Network Operations
Management, Measurement, Performance
access networks, broadband networks, BISMark, benchmarking
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HomeNets’11, August 15, 2011, Toronto, Ontario, Canada.
Copyright 2011 ACM 978-1-4503-0798-7/11/08 ...$10.00.
When shopping for Internet plans today, home users must select
their ISP primarily based on the upstream and downstream speeds
it advertises. The problem with relying solely on throughput in
advertisements is that this metric alone does not accurately reflect
the actual performance users will receive for certain applications.
Some ISPs try to make the throughput metric more accessible, by
suggesting which applications require certain throughput levels, as
shown in Figure 1. Unfortunately, this information is often inac-
curate or, worse, misleading. Although the inconsistencies in these
advertisements are troublesome, the underlying problem is that ad-
vertising a service plan based only on throughput does not capture
many aspects of network performance that can affect applications
that users care about.
We envision that users might ultimately purchase ISP service
plans based on their usage patterns, perhaps from a portal that
could recommend the appropriate service plan for a user based on
applications that are important to that user and the performance
characteristics of each plan. Realizing this vision first requires a
more precise characterization of the performance associated with
any given ISP service plan. To inform our design, we draw an
analogy with nutrition labels, which are used in many countries to
indicate the nutrients in a particular food item, so that users might
satisfy their dietary requirements. Network performance also has
many dimensions that affect various applications differently. For
example, a gamer might be more concerned with latency and jitter
than with throughput, while a user who primarily browses the Web
would be more concerned with higher throughput. Service plan ad-
vertisements should reflect these dimensions. Drawing inspiration
from recent opinions in technical policy , we develop an Inter-
net nutrition label for home broadband service plans. Just as food
nutrition labels identify the fundamental nutrients that consumers
need, we identify and justify the fundamental network properties
that consumers should be concerned about when shopping for ISPs.
The primary contribution of this paper is to initiate the debate re-
garding how users should shop for (and evaluate) their ISP service
plans. Although an Internet nutrition label touches on facets that
relate to technology, policy, and usability, the first step naturally
involves laying the groundwork for such a label. In this paper, we
focus on the important first step for designing such a label: identi-
fying the underlying network metrics that should go into an Inter-
net nutrition label, as well as how they might map to higher-level
Figure 1: Excerpts from service advertisements for AT&T DSL and U-
Verse. Intriguingly, 6 Mbits/s is sufficient for streaming video on DSL, but
for U-Verse, 18 Mbits/s is required for the same application. Full snapshots
are at: http://gtnoise.net/bismark/adverts.html.
and customer comprehensible metrics. At a minimum, such a label
should have the following properties:
• Accurate. The label should accurately reflect the network
performance that a user would experience from that service
• Measurable. Both the ISP and the user should be able to
measure the metrics associated with the label. This require-
ment also implies that the ISP should be able to engineer its
network both to measure the metric and to meet the perfor-
mance metrics that it promises to users.
• Representative of application performance. The label should
include the metrics that reflect the performance for applica-
tions that are important to the user who is shopping for the
After proposing and justifying the underlying network metrics for
an Internet nutrition label (Section 2), we use performance mea-
surements from homes to show how such a label could provide
more accurate information to a user than existing service plan ad-
vertisements (Section 3).
This paper opens many avenues for future research, such as how
such a label advertisement might be audited or enforced, and how
the metrics in the label might evolve over time. One of the most
important challenges is determining how to present the information
from the label in terms and metrics that are easy for a typical user
to understand. We discuss this challenge and other open questions
in Section 4.
2.AN INTERNET NUTRITION LABEL
This section describes and justifies the underlying metrics that
form the basis of an Internet “nutrition label”. Table 1 summarizes
these metrics. In this section, we justify each of these metrics by
demonstrating that each metric contributes to the performance that
an end user experiences for a certain application. We acknowledge
that the metrics in the nutrition label may change over time as ac-
cess technologies, applications, and their associated requirements
evolve, and as our understanding of how these metrics affect appli-
cation performance changes.
Baseline last-mile latency
Maximum last-mile latency
Loss burst length
Why it matters
Throughput for long transfers
Throughput for short transfers
Captures network load
Captures network load
Table 1: Performance metrics for the network nutrition label.
formance that an ISP can deliver to users. It indicates the amount
of data that a user can send or receive in a particular time inter-
val and thus directly determines the speed of a user’s downloads
and uploads. The throughput that a user receives can vary over
time due to various factors such as network congestion. Previous
studies have shown that users of some ISPs have lower throughput
during peak hours , either due to congestion or deliberate throt-
tling . Additionally, mechanisms such as PowerBoost [17,18]
allow users to achieve more throughput during the initial part of a
transfer, followed by a lower sustained throughput. If a user has an
ISP that offers PowerBoost, the throughput for a short download
(e.g., a Web page) might depend primarily on the throughput dur-
ing the initial interval. For long file transfers, on the other hand, the
user’s throughput will depend on the sustained throughput.
Due to these inherent sources of variability, the single through-
put metric that ISPs advertise today does not accurately reflect
the throughput that a user will receive for all types of transfers.
To better capture throughput variations, we introduce three met-
rics. Short-term throughput is the average throughput that a user
will receive during the initial period of a transfer, and sustain-
able throughput is the long-term expected throughput that a user
should receive after an initial period of higher throughput. Mini-
mum throughput captures degradation in throughput that might re-
sult from congestion, throttling, or high network utilization.
Last-mile latency The performance of most interactive applica-
tions depends on end-to-end latency . Access ISPs cannot con-
trol end-to-end latency, but they can guarantee the latency between
the home and the first border router in their network, which we call
the last-mile latency. We also define baseline last-mile latency as
the minimum latency to the border router in the ISP network. Be-
cause recent studies have shown that most modems have extremely
large buffers, which cause last-mile latencies as high as a few sec-
onds under heavy load , we also define the maximum last-mile
latency, which is the largest value of last-mile latency a user would
expect, even during periods of congestion.
To illustrate the importance of last-mile latency for the per-
formance of some applications, we show how last-mile latency
can sometimes be the dominant factor that affects Web page
download times. Figure 2 shows the average download time for
www.facebook.com for about 4,000 users across eight differ-
ent ISPs in the United States, according to data collected by Sam-
Knows and the FCC . Figure 2a plots the the average time
it takes to download all objects from www.facebook.com as
a function of the 95thpercentile of each user’s achieved down-
stream throughput. The average size of the download is 1 MByte.
95th percentile Download speed (bits/s)
Download Time (ms)
(a) Fetch time stabilizes above 6Mbits/s.
1020 3040 50
Baseline latency (ms)
Download Time (ms)
(b) Latency affects fetch times.
Figure 2: Effect of downstream throughput and baseline latency on fetch
time from facebook.com. (Data from SamKnows/FCC study.)
Although download times decrease as throughput increases, users
see negligible improvement once the throughput exceeds 6 Mbits/s:
page download time is never faster than about 500 milliseconds, re-
gardless of the user’s downstream throughput. On the other hand,
Figure 2b plots the download time for each user versus the base-
line last-mile latency for all users whose downstream throughput
(95thpercentile) exceeds 6 Mbits/s. The users’ minimum expected
download times increase by about 50% when baseline latency in-
creases from 10 ms to 40 ms. In other words, for download rates
that exceed about 6 Mbits/s, baseline last-mile latency can be the
dominant factor in determining a user’s experience when browsing
some Web pages.
Jitter Multimedia and interactive applications are sensitive to the
difference in latency between consecutive packets, which is often
called jitter  and is measured as the IP packet delay variation .
We define maximum jitter as the largest value of jitter that users
should experience. Maximum jitter has units of milliseconds and
directly represents the amount of buffering (or delay) that a stream-
ing UDP application must introduce in order to provide “smooth”
playback to a user. Based on the ITU-T recommendation that trans-
mission time for interactive applications should typically not ex-
ceed 200 ms , the maximum jitter metric can help users deter-
mine whether that plan will be suitable for interactive applications
they may wish to run.
Loss Packet loss affects the performance of all applications.
Achievable TCP throughput varies inversely with the square root
of the loss rate , and even modest packet loss rates can also
result in TCP timeouts, resulting in much worse performance. Pre-
vious work has shown that the loss of even a single packet can
severely degrade streaming video quality  (for UDP streams);
in addition, bursty packet loss can also reduce streaming video per-
formance . To capture these effects, we define the loss rate to
capture the average loss over a sustained period, and the loss burst
length to represent the average duration of a packet loss episode.
Availability Performance metrics are meaningless if a user cannot
even obtain connectivity to the Internet in the first place. Thus,
we also define availability as the fraction of time that home users
have IP connectivity to their access ISP. Although studies continu-
ally evaluate the availability of end-to-end Internet paths [1,10,16],
there has been much less focus of the availability of the access link
Usage Caps, Throttling, and Other Policies ISPs may give higher
priority to some application traffic (e.g., traffic from the ISP’s VoIP
or IPTV service) and discriminate against others (e.g., some ISPs
have been found to throttle peer-to-peer traffic). Some ISPs also
impose usage caps, where the performance or price of the network
access changes after a user transfers more than a certain volume of
traffic in a month. The Internet nutrition label should make these
policies transparent to users. The metrics in Table 1 do not directly
capture these types of policies, but we argue that an ISP could (and
should) expose these policies by advertising multiple separate nu-
trition labels for conditions where performance would vary drasti-
cally (e.g., for a certain application, after a cap has been imposed).
3.APPLYING THE LABEL IN PRACTICE
The Internet nutrition label has two purposes. First, when users
shop for Internet service plans, the label informs them about the
performance they can expect to receive. After a user subscribes to
a given plan, the label could also be used for auditing. For this pur-
pose, a benchmarking suite could be installed on the user’s home
gateway. Auditing has its own set of challenges, which we discuss
briefly in Section 4 but do not address in this paper. We focus on
how ISPs obtain values for the Internet nutrition label before users
holds across three different ISPs. We highlight, in particular, that
two homes with the same advertised service plan can, in fact, see
very different performance with respect to the metrics associated
with the Internet nutrition label.
Before a user purchases a service plan, ISPs must estimate the
metrics in the Internet nutrition label, based on the user’s address
or phone number. Unfortunately, it is difficult for an ISP to pre-
dict precisely the performance a user will receive, since the perfor-
mance can be affected by the quality of the local loop, wiring in
the home, and even the user’s home network equipment. To ac-
count for these uncertainties, we propose that the ISP advertise an
expected range of values for each metric. A potential customer will
typically have the local loop tested before buying a plan; in many
countries, ISPs already perform simple tests in the local loop to
determine which plans are available to a user. To construct the In-
ternet nutrition labels, ISPs would have to run more comprehensive
tests and expose the results to the user. A simple test is sufficient to
estimate the latency and throughput that the customer can expect to
achieve, but other factors such as loss and jitter are more difficult
to predict. ISPs might be able to estimate such metrics based on
measurements from other subscribers in the same neighborhood.
We now show examples of Internet nutrition labels for six house-
holds across three different ISPs. Table 2 shows most metrics
for the network nutrition label experienced by each user over the
course of one month, using data that we collected from an instru-
mented router at each home. In particular, we show sustainable and
short-term throughput, minimum throughput, baseline and maxi-
mum last-mile latency, loss rate, and an approximation of jitter.
We have redacted the identities of the ISPs for this example, since
these numbers are not intended to be conclusive performance re-
ports from each ISP; rather, these data points serve as an illustra-
within the same service plan.
The first row of Table 2 shows that the sustainable through-
put for the WiMAX and DSL users roughly matches the adver-
tised upstream and downstream throughput values, but sustainable
throughput for the cable users is significantly less than the adver-
tised values, since the cable ISP advertises the throughput associ-
ated with PowerBoost. The cable users experience higher short-
term throughput than sustainable throughput, also due to Power-
Boost. The DSL and WiMAX ISPs do not offer PowerBoost-like
technology, so in these cases users do not experience higher short-
term throughput. Figure 3 shows the throughput profiles for two
cable users with PowerBoost; the graph shows a transient period
where users achieve higher download and upload throughput. In-
terestingly, the peak rates during this initial transient period differ
significantly, even between these the two users, suggesting that a
nutrition label could provide useful information about differences
that exist within a single advertised service plan. Some previous
studies have shown that a user’s upload and download throughput
may degrade during periods of peak load or congestion , so we
believe that including this metric is important. For the users we
studied, there was no discernible degradation due to time of day, so
we assume that the minimum throughput is the same as the adver-
tised throughput. This may not hold in practice.
Figure 4 shows that the DSL and cable users experience rela-
tively low baseline latencies compared to the WiMAX user. The
latency profile that we observe for the WiMax user also agrees with
an independent study conducted in a different market . Because
we did not capture packet-level round-trip times for the measure-
ments we conducted in our deployment, we approximate the jitter
in Table 2 as the standard deviation of the last mile latency; again,
we see that the WiMAX user experiences jitter that is significantly
higher than users with other access technologies.
Loss rates do not appear to correlate with access ISP, technol-
ogy, or service plan. Rather, the most important factor for affecting
loss rates appears to be the quality of the local loop: a noisy lo-
cal loop may have higher loss. For DSL, local-loop “interleaving”
is sometimes enabled to reduce the effect of line noise, which can
sometimes increase the baseline last-mile latency by a factor of two
or more. For example, consider DSL users 2 and 3: user 2 has low
1This user actually gets 22 Mbits/s for 3s and 18.5 Mbits/s for 10s.
We label this as 18.5 Mbits/s for 13s for visual ease of reading.
Cable User 1
Cable User 2
(a) PowerBoost for download.
Cable User 1
Cable User 2
(b) PowerBoost for upload.
Figure 3: PowerBoost effects across four distinct Comcast users. The du-
ration and rates vary across users.
020 4060 80100120140
Last mile latency (ms)
AT&T DSL user
Figure 4: Latency profile for Cable, DSL and WiMAX.
baseline latency (8 ms), but much higher loss (0.5%) compared to
user 3, who has low loss (0.1%), but higher latency (24 ms).
Table 2 shows that all users experience a maximum last-mile
latency that is significantly higher than the baseline last-mile la-
tency. For these users, the latency is a direct function of the modem
buffer size (which depends on the manufacturer), and the through-
put (which affects how quickly the buffer drains). DSL users 2 and
3 see latencies of about two seconds (extremely high, especially
for interactive traffic). Although ISPs are not directly responsible
the modem that a user installs in the home, ISPs in many coun-
tries (including in the United States and in Europe) do sell or rent
modems directly to users. In light of the significant effect that a
user’s choice of modem has on overall performance, it may ulti-
mately make sense for an ISP to include information about how
different modems might affect this metric.
We have identified several metrics that we believe should com-
a starting point. In this section, we outline several avenues for fu-
ture research that touch on technology, policy, and usability.
Presenting information to users An Internet nutrition label should
allow users to understand what the label means. To reconcile the
tension between the low-level metrics (such as those in Table 1)
and concepts more meaningful to users, we envision that network
6 Mbits/s down
1 Mbits/s up
DSL user 1
6 Mbits/s down
0.50 Mbits/s up
DSL user 2
3 Mbits/s down
0.38 Mbits/s up
DSL user 3
3 Mbits/s down
0.38 Mbits/s up
Cable user 1
22 Mbits/s down
4 Mbits/s up
22 down for 8s
3.8 up for 20s
Cable user 2
22 Mbits/s down
4 Mbits/s up
18.51down for 13s
7.0 up for 8s
Sustainable throughput (Mbits/s)
Short-term throughput (Mbits/s)
Minimum throughput (Mbits/s)
Baseline last-mile latency (ms)
Maximum last-mile latency (ms)
Loss rate (%)
Loss burst length
Table 2: Labels for users from different ISPs and service plans. The highlighted columns indicate metric values that simpler advertising models would not
capture. The measurements we take in our deployment do not allow us to compute loss burst length.
Table 3: Different users exhibit different usage profiles. The table shows
traffic volumes from February 6–20, 2011 for two users in our testbed.
User 1 has more ssh and secure IMAP traffic, whereas User 2 has con-
siderably more HTTP traffic and higher overall traffic volumes.
metrics could be communicated to users in raw or interpreted form
depending on the circumstance. Raw metrics, such as those defined
in Table 1, could be used to specify network-level requirements
for applications (much as electronic devices expose electrical “in-
put” requirements in standardized formats on adapter labels (e.g.,
“AC100-240V, 50-60Hz 1A”)). While raw metrics may not be im-
mediately meaningful to typical consumers, a user can easily un-
derstand that finding an electricity source that matches the ranges
required by the device will ensure performance. In such cases, a
single baseline or range requirement for the metrics would be suf-
Interpreted metrics are analogous to the FDA’s mandate of a
“Daily Recommended Intake” column for each nutrient, based on a
typical individual’s daily nutritional needs. Applying this approach
directly to the network scenario is challenging, since users have dif-
ferent requirements and expectations for application performance.
In practice, though, a user may more easily understand interpre-
tations such as how long a song download will take (in terms of
time), or what the quality of a streaming movie of a streaming
movie would be (in terms of frame rate). An important area for
future research will be to generalize a mapping from the low-level
elements of the network nutrition label to metrics and characteris-
tics that are meaningful to users.
Accounting for user profiles and preferences Another approach
is to hide metrics from users altogether and instead focus on users’
actual networking needs and expectations. User needs could be
captured by asking each home about their application usage or ac-
tivity levels, thereby building user profiles unique to each individ-
ual or household. These profiles would allow users to better un-
derstand and describe themselves and their needs to others. ISPs
could directly map these needs to existing offerings without having
to expose users to actual metrics.
Our deployment study in Atlanta shows that households exhibit
very different usage profiles. Table 3 shows the traffic usage break-
down by network port for two households. For User 1, about 10%
of the total downstream traffic is ssh traffic, whereas User 2 has
almost no ssh traffic. User 1 is also a considerably heavier email
user, with nearly half a gigabyte of downstream traffic for secure
IMAP. On the other hand, User 2 has higher overall usage and
downloads much more traffic via HTTP.
Although usage patterns for the two households in Table 3 in-
dicate that the households use applications differently, traffic vol-
ume does not suggest the importance of different applications to
each household. Given the opportunity, users might prioritize ap-
plications differently (e.g., a high-quality voice and video calls to
a loved one might be more important than the ability to quickly
download a large file). Ultimately, the nutrition label should cap-
ture these priorities, and potentially even resolve the priorities of
multiple members from a single household. Taking into account
these priorities will help users understand their own home Inter-
net requirements, identify suitable service plans, and evaluate the
performance of any particular ISP service plan.
Dynamics Although the phrase “nutrition label” implies a static
representation of performance, network conditions are dynamic
(i.e., performance is affected by many factors such as time of day
and congestion), and users’ requirements may also change over
time. Thus, a “label” that reflects ISP performance may be best
represented as an interactive application that allows users to view
performance for multiple applications over different time intervals
(e.g., an hour, a week, or a month). Designing the label to represent
these dynamic conditions is an open area for future work.
Auditing Advertising the characteristics of ISPs’ available service
plans are, of course, only one side of the story: users will also
want to know whether ISPs actually meet the performance met-
rics that they advertise. In fact, the metrics in the label bear some
resemblance to service level agreements (SLAs) for backbone net-
works , which are binding contracts that guarantee bounds on
latency, packet loss, and availability . Due to the inherent vari-
ability introduced by different access technologies, network condi-
tions, usage patterns, and cases where the access link might be the
bottleneck, Internet nutrition labels cannot currently be tight con-
tracts. Indeed, the question of how an Internet nutrition label might
be audited raises many questions, such as who should perform the
auditing, how it should be conducted, and what conditions would
deem an ISP in violation of its advertised nutrition label. Regu-
latory agencies across the world are already grappling with these
issues with varying degrees of success [2,4].
Household “network usage personas” A community-oriented ap-
proach to sharing the Internet nutrition labels might help users learn
about different ISP service plans in a geographic area. For exam-
ple, households might contribute “network usage personas” that de-
scribe the profile of network use in a household. Other households
could then use these personas to find similar users for each service
plan. Currently, end-users are typically limited to discussions of
plan costs, the quality of customer support, and network outages,
which are important factors but may not completely represent the
level of service that they might expect. Being able to understand
the details of the service they are purchasing in terms of an Internet
nutrition label would allow users to rate and compare their service
plans in more meaningful ways.
Application-specific labels Although the totality of home network
usage drives plan selection, some applications may warrant their
own labels. For example, by giving applications that are partic-
ularly intensive on the home network their own label, customers
may become more aware of how competing applications affect per-
formance. A significant challenge for end-users is understanding
the consequences of making changes to their networks. The addi-
tion of a new device or service can have significant effects on the
network performance, which can be difficult for non-specialists to
deduce. These users see a change in performance but are not sure
why it has happened or whether they can do anything about it after
the point of purchase. Having labels that let end-users ask ques-
tions about whether their current service plan can handle the costs
of a particular application could empower people to make changes
to their network that are commensurate with their ISP service plan.
We thank the participants in the SamKnows and BISMark studies,
and to Sam Crawford at SamKnows and Walter Johnston at the
FCC for help and access to the data from the SamKnows study. We
are grateful to Dave Täht for his continuing support of the BIS-
Mark platform. This project is supported by the the National Sci-
ence Foundation through awards CNS-1059350, CNS-0643974, a
generous Google Focus Grant, the European Community’s Seventh
Framework Programme (FP7/2007-2013) no. 258378 (FIGARO),
and the ANR project C’MON.
 D. G. Andersen, H. Balakrishnan, M. F. Kaashoek, and R. Morris. Resilient
Overlay Networks. In Proc. 18th ACM Symposium on Operating Systems
Principles (SOSP), pages 131–145, Banff, Canada, Oct. 2001.
 N. Anderson. What if ISPs had to advertise minimum speeds? In Hungary, they
 N. Anderson. Does Broadband Need Its Own Government Nutrition Label?
ars, Oct. 2010. Ars Technica.
 Ofcom wants to ban misleading broadband speed ads.
 K.-T. Chen, P. Huang, and C.-L. Lei. How sensitive are online gamers to
network quality? Commun. ACM, 49:34–38, November 2006.
 T. Demichelis and P. Chimento. IPPacket Delay Metric. Internet Engineering
Task Force, Nov. 2002. RFC 3393.
 M. Dick, O. Wellnitz, and L. Wolf. Analysis of factors affecting players’
performance and perception in multiplayer games. In Proceedings of 4th ACM
SIGCOMM workshop on Network and system support for games, NetGames
’05, pages 1–7, New York, NY, USA, 2005. ACM.
 M. Dischinger, A. Haeberlen, K. P. Gummadi, and S. Saroiu. Characterizing
residential broadband networks. In Proc. ACM SIGCOMM Internet
Measurement Conference, San Diego, CA, USA, Oct. 2007.
 M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi. Detecting
bittorrent blocking. In Proc. Internet Measurement Conference, Vouliagmeni,
Greece, Oct. 2008.
 N. Feamster, D. Andersen, H. Balakrishnan, and M. F. Kaashoek. Measuring
the effects of Internet path faults on reactive routing. In Proc. ACM
SIGMETRICS, San Diego, CA, June 2003.
 N. Feamster and H. Balakrishnan. Packet loss recovery for streaming video. In
Proc. 12th International Packet Video Workshop (PV 2002), Pittsburgh, PA,
 J. Gettys. Bufferbloat. http://www.bufferbloat.net/.
 One-way transmission time recommendation, 2003.
 J. Martin and A. Nilsson. On service level agreements for ip networks. In
Proceedings, IEEE INFOCOM, 2002.
 J. Padhye, V. Firoiu, D. Towsley, and J. Kurose. Modeling TCP Throughput: A
Simple Model and its Empirical Validation. In Proc. ACM SIGCOMM, pages
303–323, Vancouver, British Columbia, Canada, Sept. 1998.
 V. Paxson. End-to-End Routing Behavior in the Internet. IEEE/ACM
Transactions on Networking, 5(5):601–615, 1997.
 PowerBoost FAQs - Charter. http://myaccount.charter.com/
 PowerBoost FAQs - Comcast. http://www.comcast.com/Customers/
 SamKnows. Measure Your Broadband Accurately.
 Sprint SLA. https://www.sprint.net/sla_performance.php.
 S. Tao, J. Apostolopoulos, and R. Guerin. Real-time monitoring of video
quality in ip networks. Networking, IEEE/ACM Transactions on, 16(5):1052
 K. Yap, S. Katti, G. Parulkar, and N. McKeown. Deciphering a Commercial
WiMAX Deployment using COTS Equipments. Technical report, Stanford