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# Social Distancing and the Internet: What Can Network Performance
Measurements Tell Us?

Jessica De Oliveira Moreira1, Amey Praveen Pasarkar2, Wenjun Chen2, Wenkai
Hu2,
Jan Janak3, Henning Schulzrinne3 Email<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Jessica De Oliveira Moreira, Amey Praveen Pasarkar, Wenjun Chen, and Wenkai Hu
contributed equally to this research. The work of Jessica De Oliveira Moreira
was supported by Craig Newmark Philanthropies. Corresponding authors: Jan
Janak; Henning Schulzrinne. 1Barnard College, New York, USA  2Fu Foundation
School of Engineering and Applied Science, Columbia University, New York, USA
3Department of Computer Science, Columbia University, New York, USA

###### Abstract

The COVID-19 pandemic and related restrictions forced many to work, learn, and
socialize from home over the internet. There appears to be consensus that
internet infrastructure in the developed world handled the resulting traffic
surge well. In this paper, we study network measurement data collected by the
Federal Communication Commission’s (FCC) Measuring Broadband America (MBA)
program before and during the pandemic in the United States. We analyze the
data to understand the impact of lockdown orders on the performance of fixed
broadband internet infrastructure across the US, and also attempt to correlate
internet usage patterns with the changing behavior of users during lockdown.
We found the key metrics such as change in data usage to be generally
consistent with the literature. Through additional analysis, we found
differences between metro and rural areas, changes in weekday, weekend, and
hourly internet usage patterns, and indications of network congestion for some
users.

###### Index Terms:

Computer networks, internet.

##  I Introduction

Much has been written about whether the residential internet infrastructure in
the United States can cope with the sudden conversion of work into tele-work,
school into distance learning and movies into Netflix binging, albeit usually
based on data of dubious quality. This topic has seen renewed interest in 2020
when the COVID-19 pandemic suddenly forced a large percentage of the US
population to work, learn, and socialize from home over the internet for
extended periods of time.

Network measurements based on data from a variety of vantage points tell us
about the performance of internet infrastructure. However, such measurements
may also tell us about the effort and success of social distancing, mobility
trends and patterns, the ability of different communities to move to internet-
mediated modes of interaction and their longer-term sustainability.

We attempt to address both questions in this paper. Drawing on the Federal
Communications Commission’s (FCC) Measuring Broadband America (MBA) program
dataset [1], available longitudinally since 2011, we estimate the impact of
increased internet usage during the pandemic on a variety of performance
metrics. We specifically look at throughput and packet loss, disaggregating by
coarse-grained geography, population density, and time period. Even though it
usually does not draw much attention, we are particularly interested in
upstream performance, as much more intense use of video conferencing tools has
been a trend during the pandemic.

At least as important as measuring the impact of COVID-19 on internet
performance is using indirect metrics, such as data usage, to estimate the
success of social distancing measures, while maintaining user privacy. We draw
on two metrics to explore whether we can detect the daytime and evening usage
of residential internet users, indicating work from home or distance learning,
as well as observance of stay-at-home orders on weekends.

The MBA program records user network data usage, and also suspends throughput
measurements during periods of significant network activity, such as video
conferencing. Thus, we might be able to deduce when households are actively
using their fixed broadband connection and whether such usage reflects, for
example, the earlier restrictions imposed in Washington State or in the New
York metro area.

Our key findings can be summarized as follows:

  * •

We find a significant increase in data usage, consistent with lockdown
timeline and observations made by other researchers.

  * •

The traffic spike in rural areas is initially shorter and less pronounced than
in metro areas, however, we see the trend start to reverse by June 2020.

  * •

Our data suggest that users in metro areas react to government recommendations
faster and more drastically than users in less populated areas.

  * •

We see a significant increase in daytime weekend traffic from March to May
2020. By June the levels return to their normal pre-lockdown levels,
suggesting that users might have complied with stay-at-home recommendations
only initially.

  * •

Consistent with others, we see the weekday daytime traffic pattern morphing
into the weekend pattern.

  * •

We see a slight 5% decrease in average speed for about 60-70% of users and
also found a significant almost 50% increase in packet loss for some users in
mid-March 2020, suggesting that increased internet usage may have caused
network congestion.

Our study is based solely on the FCC MBA dataset which has known limitations.
For example, it only covers the largest ISPs and omits fixed wireless and
satellite providers, the latter by their choice. We also briefly discuss how
more systematic data gathering during both short-term natural disasters and
long-term epidemics could supplement our understanding of how households deal
with such crises, providing information to policy makers in near real-time and
in a privacy-respecting manner.

Several authors published similar studies already while the pandemic was still
ongoing. Existing studies are based on data from network services [2, 3, 4],
ISPs and internet exchange points [5, 6], or mobile networks [7]. Our work
aims to complement such efforts. We study similar metrics using data collected
by US-based fixed broadband internet users participating in the FCC MBA
program.

The rest of the paper is organized as follows. We review related work on
COVID-19 impacts on the internet in Section II. The datasets used in all our
analyses are discussed in Section III. We describe our methodology in Section
IV. In Section V we examine broadband internet usage during lockdown in
metropolitan and rural counties. Section VI studies hourly data usage patterns
before and during lockdown. In Section VII, we look at the effects of COVID-19
lockdown on fixed broadband upload and download speeds. Section VIII examines
packet loss during lockdown. We discuss our findings in Section IX and
conclude and discuss future work in Section X.

##  II Related Work

A small number of studies focusing on the effects of lockdowns on internet
infrastructure have already been published as of November 2020. These studies
typically look at traffic trends and patterns from various vantage points.
Some look for signs of stress in internet infrastructure, while others attempt
to correlate traffic data with user behavior during lockdowns.

Feldmann et al. studied the effects of lockdowns on internet traffic using
traffic data obtained from several Internet Service Providers (ISPs) and
Internet Exchange Points (IXPs) in Europe and the US East Coast[5]. The
authors found that residential traffic increased by 15–20% after the lockdown
and that remote work and education applications saw a 200% increase in
traffic. The regular weekday traffic pattern morphed into a weekend-like
pattern with a significant traffic increase starting in the morning, from mid-
March until roughly mid-May.

Böttger et al. studied similar effects using data from Facebook’s global edge
network [2]. They observed a traffic increase in the second half of March and
found regional correlations between traffic growth and the spread of COVID-19
in the region. The data from Facebook’s video services showed a degradation in
the quality of user experience in India and South Africa. Authors attributed
the degradation to limited capacity and last-mile network congestion in those
countries.

Liu et al. found a 30–60% increase in peak traffic volumes in the US, a
statistically significant increase of 10% in average latency over two months,
and ISPs adding interconnect capacity at more than twice the usual rate [6].

Several broadband market reports analyzed the state of broadband
infrastructure during the pandemic. OpenVault saw a 40% broadband traffic
increase in Q3 2020 over 2019, but less than 1% over Q2 2020 [8]. Traffic
growth plateaued in 2020, indicating a new normal for broadband bandwidth
usage. Temporarily relaxed data usage quota resulted in faster growth of
usage-based accounts compared to flat rate accounts. Ofcom reported that UK
broadband service providers were generally able to keep up with the demand in
March, with only a 1–2% decrease in download and upload speeds and 2% increase
in latency [9]. SamKnows published a series of articles about the state of
critical infrastructure [10, 11, 12, 13]. Their observations in UK and the US
showed only slightly worse average download speeds and round trip times during
lockdown.

Several network service providers discussed lockdown-related trends and
observations on their blogs: Google [14], Akamai [3], Facebook [4], and
Comcast [15]. In all cases, initial traffic surges aligned with mandatory
isolation protocols have been reported, as well as long-term shifts in daily
and weekly traffic patterns. The general sentiment appears to be that existing
infrastructure was either able to keep up with the traffic due to over-
provisioning, or could be rapidly adapted to meet the increased demand. It is
worth mentioning that network service providers generally report higher
traffic surges than broadband monitoring companies like SamKnows or OpenVault.

NCTA reported a usage growth of 30% (downstream) and 50% (upstream) since
March among its member broadband cable providers [16]. Sandvine saw a traffic
growth of 40% between February and April. The growth was driven by video,
gaming, and social sharing applications which comprise 80% of all traffic
[17].

Kovacs compared the performance of fixed broadband internet during the
pandemic in the US and in Europe [18]. Using data collected by the company
Ookla, the author found that US fixed broadband networks generally performed
better than their comparable European peers, offering 30–35% higher mean
download speed. Kovacs attributed the difference to bigger long term
investments in telecommunications infrastructure, more favorable mix of
technologies, and lighter-touch regulatory approach in the US.

Lutu et al. studied traffic patterns obtained from a UK-based mobile network
operator (MNO) and analyzed the effect of mandatory stay-at-home orders on
users’ mobility behavior [19]. They found an overall decrease in users’
mobility following the government’s mandatory stay-at-home orders. The authors
found a decrease in cellular data traffic volumes and an overall increase in
voice traffic.

Table I: Lockdown dates of all 50 US states in an increasing order (left-right, top-bottom) CA | NY | IL | NJ | CT | LA | OH | OR | DE | IN | MA | MI | NM | VT | WA | WV | HI | ID | WI
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
3/19 | 3/20 | 3/21 | 3/21 | 3/23 | 3/23 | 3/23 | 3/23 | 3/24 | 3/24 | 3/24 | 3/24 | 3/24 | 3/24 | 3/24 | 3/24 | 3/25 | 3/25 | 3/25
CO | KY | MN | NH | AK | MT | RI | KS | MD | NC | VA | AZ | TN | OK | NV | PA | FL | ME | TX
3/26 | 3/26 | 3/27 | 3/27 | 3/28 | 3/28 | 3/28 | 3/30 | 3/30 | 3/30 | 3/30 | 3/31 | 3/13 | 4/1 | 4/1 | 4/1 | 4/2 | 4/2 | 4/2
GA | MS | AL | MO | SC | AK | IA | NE | ND | SD | UT | WY |
4/3 | 4/3 | 4/4 | 4/6 | 4/7 | — | — | — | — | — | — | — |

##  III Datasets

The Federal Communications Commission (FCC) runs a program called Measuring
Broadband America (MBA) which collects data about broadband internet usage and
performance in the United States [1]. The data are collected by a panel of
volunteers with a hardware test unit installed on their fixed broadband
internet connection. The test unit is known as the whitebox and is
manufactured by SamKnows [20]. The panel consists of 4000 to 5000 volunteers
(depending on the year), selected by the FCC to include 16 major ISPs,
geographic regions, and broadband connection types [21]. The test unit uses a
combination of passive and active measurement techniques to determine the
state, parameters, and usage of the internet connection repeatedly throughout
the day.

Since 2011, the FCC has been publishing annual reports on the overall
performance of the US fixed broadband infrastructure [22]. The reports are
based on the data collected from a representative subset of the test units,
cross-validated with information provided by fixed broadband ISPs. The reports
cover approximately 80% of the US population.

In addition, the FCC regularly publishes anonymized raw datasets on its
website [23]. All deployed test units generate approximately
$20\text{\,}\mathrm{GB}\text{/}$ of data per month. In this study, we use the
raw datasets to analyze the performance and changing usage patterns of US
fixed broadband before and during COVID-19 related government-imposed
restrictions (lockdowns). We work with the raw data collected from January
2019 to June 2020. At the time of writing, the latest dataset published by the
FCC was from June 2020.

The raw datasets are published in the form of comma-separate values (CSV)
files broken down by the month and type of measurement. The following
measurements are included: upload and download speed, the time to fetch a few
selected well-known web pages, UDP/ICMP latency when idle and under load,
video streaming performance, VoIP performance, DNS performance, and total
bytes uploaded and downloaded by the user. In this project we only use the
following raw data subset: upload and download speed, total user bytes, and
packet loss.

In addition to raw datasets, we also use the unit profile, unit census, and
excluded units datasets as published by the FCC. The unit profile dataset
provides additional information about the test units, including ISP name and
service tier, technology, and coarse location. Since some of the information
in the unit profile dataset must be validated manually, not all test units are
included. The unit census dataset identifies the census block for each test
unit. The excluded units dataset lists the test units that were excluded from
the most recent annual report. At the time of this study, the most recent unit
profile, unit census, and excluded unit datasets were from 2018, i.e.,
considerably older than the raw datasets. As a result, we must limit some of
our analysis to the subset of the test units (and hence users) that have
profile and census data.

Analysis that involves population demographic information integrates 2019 U.S.
Census data [24]. The Census provides county-level population information for
all counties in the US and can be linked to the MBA data through the
aforementioned unit profile datasets.

The raw datasets include measurements from 7539 test units from across the US.
The number includes all test units that reported at least once between January
2019 and June 2020. The unit profile dataset provides additional information
about 4378 test units. The unit census dataset contains 4366 test units. The
excluded unit dataset contains 1656 test units. The total size of all datasets
used in our analysis was over $300\text{\,}\mathrm{GB}\text{/}$.

By analyzing these datasets, we are aiming to understand not only how the
fixed broadband internet infrastructure performed, but also whether we can
learn anything about people’s changing behavior over the course of the
pandemic.

###  III-A Ethical and Privacy Considerations

The FCC MBA panel consists of volunteers who were informed about the goals and
methodology of the program, explicitly opted in, and provided written consent
to participate. All publicly released FCC MBA datasets have any personally
identifiable information removed. For example, the unit census dataset which
provides a coarse location for each test unit aggregates to blocks with no
fewer than 1000 people for privacy purposes.

All additional datasets used in this study, e.g., the U.S. Census Bureau data,
is public information and can be obtained freely from the internet.

##  IV Methodology

In order to analyze the impacts of COVID-19 lockdowns on fixed broadband
internet usage, we need to compare data from time periods before and during
lockdown. In the US, individual states issued statewide lockdown orders on
different dates. Table I shows the first date of the statewide lockdown order
being enforced for each of the 50 US states. The first state to issue a
lockdown order was California on March 19. The last statewide lockdown order
was issued by South Carolina on April 7. The seven states at the end of the
table did not impose lockdown orders.

For the purpose of the analysis presented in this paper, we use January and
February 2020 as the pre-lockdown period and mid-March to mid-May as the
lockdown period. We consider the period from mid-May until end of June as
post-lockdown, i.e., a period where many of the restrictions were being
relaxed. To eliminate seasonal effects, we also use data from 2019 to serve as
a baseline for comparison in some analysis. Thus, most of our algorithms run
on the data from January 2019 to June 2020 (the last dataset published by the
FCC as of September 2020).

We uploaded the FCC MBA raw data from January 2019 to June 2020, the 2019
census data, and all the auxiliary datasets (unit profile, unit census,
excluded units) into a Google Cloud BigQuery database [25]. BigQuery allowed
us to query the data using a familiar SQL-based interface.

We then analyzed the data using Google Cloud Datalab [26] running on virtual
machines (VMs) in Google Cloud. Google Cloud Datalab is a version of
JupyterLab [27] with Google Cloud specific extensions (e.g., libraries to
access the BigQuery database). All our programs to analyze the data were
written in Python 3. We kept everything synchronized and organized in a shared
Git repository.

To calculate the total downloaded data per user, we sum the number of bytes
received from the internet on all wired LAN and Wi-Fi networks. Similarly, to
calculate the total uploaded data per user, we sum the number of bytes
transmitted to the internet from all wired LAN and Wi-Fi networks. In both
cases, any data received or transmitted by the test unit itself during active
measurements is excluded.

###  IV-A Data Cleaning

The raw dataset includes measurements from all test units that reported at
least once within the analyzed time period. We found several test units
reporting invalid data, e.g., unrealistic amounts of user traffic. We assumed
those test units were malfunctioning and manually removed all their data from
the database.

In some of the experiments where a larger number of test units was not
necessary, we filtered the raw datasets and included only test units that were
not found in the excluded units datasets and that were found in the unit
profile dataset. This step gives a smaller test unit subset that roughly
includes the test units used in the most recent FCC annual report.

###  IV-B Population Demographics Preprocessing

For population demographics analysis, we need to classify test units as
belonging to either metropolitan or rural areas. We describe our approach in
this section.

The unit census FCC MBA dataset provides Federal Information Processing
Standard (FIPS) census object identifiers for a subset of the test units in
the study. The census object can be one of: block group, tract, county, state.
For privacy reasons, the dataset only provides census objects with more than
1000 people. For example, if a test unit belongs to a census block group with
fewer than 1000 people, a census tract will be used for the test unit instead.
If the tract still has fewer than 1000 people, granularity decreases to county
and eventually state level.

Census block groups are designed to contain a specific number of people and
therefore provide little information regarding how densely populated an area
is. For example, a few blocks in New York City would correspond to a single
census block, while an entire town in rural America would also qualify as a
census block. Therefore, in order to support an analysis of how users in more
densely populated areas behaved relative to those in less densely populated
areas, we focus on county-level populations.

FIPS census identifiers have a hierarchical structure where the first two
digits refer to the state, while the subsequent three refer to the county
within the state. Thus, to map test units to populations, we extract the
county from each test unit’s FIPS census identifier and map it to a population
using the publicly-available 2019 US Census Bureau dataset [24]. In this step,
we must exclude approximately 180 test units that have a state-level census
identifier only (counties with fewer than 1000 people).

Among the test units for which we were able to obtain county population, we
focus specifically on those that have participated in the study since January
2019 (1692 test units in total), i.e., we exclude test units that have joined
the FCC MBA program later. This helps ensure that when we compare 2019 and
2020 data, any changes that we observe will be due to external factors like
the pandemic and not due to changing test unit sample size.

We classify test units as belonging to a ’Metro’ area if their corresponding
county population is greater than $1,000,000$ ($n=396$). Similarly, we
classify test units as belonging to a ’Rural’ area if the county population is
less than $100,000$ ($n=342$). It should be noted that these thresholds are
not being applied at the county level. An accepted convention, when
considering populations, is to refer to higher resolution “tracks of land”
(e.g., municipalities) [28]. We choose our own thresholds that are consistent
with general breakdown of the US population: about $20\%$ of the US population
resides in rural areas. With our choice, we also have approximately $20\%$ of
the dataset being classified as rural [29]. Our choice of the metro threshold
allows us to select the top $50$ most populous metropolitan subset while also
ensuring a similar sample size to the rural subset [28].

###  IV-C Daily Patterns Preprocessing

All measurement timestamps in the FCC MBA raw datasets use the Coordinated
Universal Time (UTC). The unit profile dataset provides a timezone offset for
each test unit. We used that information to convert UTC timestamps to the test
unit’s local time. This step reduced the number of test units available for
daily patterns analysis to approximately 3000.

For some test units and months, a daylight savings time conversion should also
have been performed. We omitted this step as it would not have changed our
daily traffic patterns significantly.

For each month, we define the average downloaded ($D$) and uploaded data ($U$)
as

$\overline{D}=\frac{SUM(\text{download data})}{COUNT(\text{test unit})}$ (1)
$\overline{U}=\frac{SUM(\text{upload data})}{COUNT(\text{test unit})}$ (2)

where “test unit" refers to test units that kept sending measurements from
January 2019 to June 2020, i.e., we focus on test units that have been running
for over a year and can thus be considered stable.

Figure 1: Graph showing the monthly average downloaded data per user from
January 2019 to June 2020, partitioned by test unit county population. Figure
2: Graph showing the daily downloaded data per user in March 2020 for those in
metro and rural areas, as well as LA county.

##  V Data Usage by Population Demographics

###  V-A Average Monthly Downloads

A high-level overview of the effect of COVID-19 on fixed broadband internet
usage can be seen by analyzing the average monthly downloaded data per user.
As shown in Figure 1, we notice a sizable increase in overall downloaded data
in the months of March, April, and May 2020. This increase is consistent
across both metro and rural counties, although it is more pronounced in metro
areas. The three pandemic months are the highest-usage months for metro areas.
In contrast, rural areas only have one month larger than their pre-pandemic
maximum of December 2019.

Interestingly, in the fourth month of the pandemic, we notice a drop in
overall downloaded data in metro areas, while rural areas remain constant.
This drop coincides with the relaxation of many stay-at-home orders, indoor
dining bans, and curfews in cities across the United States [30, 31].

###  V-B Average Daily Downloads

Focusing on daily fixed broadband internet usage can allow us to understand
the public’s reactions to various events. In particular, by analyzing daily
averages over the course of March, we can better understand the reactions to
the various stay-at-home orders, school shutdowns, and curfews from an
internet usage point-of-view.

To this end, we present in Figure 2 the daily average downloaded data usage
for both metro and rural users. The first event we look to analyze is the
White House first announcing its social distancing recommendations on March 16
for the entire country [32]. Following this announcement, we observe that
users in metro areas use a significantly larger amount of data than those in
rural areas. In the first half of the month, there was no such difference
(Figure 2). It is unsurprising that those in cities and other metro areas
reacted to this recommendation more drastically than those in less populated
areas. Cities such as New York City, San Francisco, and Los Angeles (LA) were
among the hardest hit areas in the early weeks of the pandemic [33].

For many of the aforementioned cities, various levels of lockdowns were put in
place on top of the White House’s recommendations I. Therefore, we
additionally investigate if we can correlate these local ordinances with
county-level usage patterns. Los Angeles (LA) county has the most test units
in the FCC MBA dataset ($n=44$), so we examine the daily average downloaded
data per user for all those residing in LA (Figure 2).

We observe the first large spike on March 14, the day after many schools in
the county shutdown [34]. March 14 was a Saturday, so this spike is not due to
remote learning. However, if schools were shut down by this date, then we can
reasonably expect that various other aspects of society were shut down. For
example, although the official order to ban indoor dining came later, by March
14, many LA restaurants were already closed[35]. This would suggest that the
spikes we observe following government orders are due to people seeking
entertainment from the internet over outside options.

We also notice a larger spike in the days following the March 16 White House
stay-at-home order.

(a) Pre-lockdown weekdays. Average volume of downloaded data per test unit,
broken down by the hour of the day, on weekdays in the pre-lockdown time
period.

(b) Pre-lockdown weekends. Average volume of downloaded data per test unit,
broken down by the hour of the day, on weekends in the pre-lockdown time
period.

(c) Lockdown weekdays. Average volume of downloaded data per test unit, broken
down by the hour of the day, on weekdays in the lockdown time period.

(d) Lockdown weekends. Average volume of downloaded data per test unit, broken
down by the hour of the day, on weekends in the lockdown time period.

(e) 2019 vs 2020 weekdays. Average volume of downloaded data per test unit on
weekdays in March-June compared between 2019 and 2020.

(f) 2019 vs 2020 weekends. Average volume of downloaded data per test unit on
weekends in March-June compared between 2019 and 2020.

Figure 3: Graphs comparing and contrasting the average hourly downloaded
volume of data per test unit in various time periods. The peak is always
between 18:00 and 22:00, which is the internet rush hour. The volumes in the
lockdown time period are generally larger than the volumes in the pre-lockdown
time period. Lockdown weekday traffic patterns start to resemble weekend
patterns.

(a) Pre-lockdown weekdays. Average volume of uploaded data per test unit,
broken down by the hour of the day, on weekdays in the pre-lockdown time
period.

(b) Pre-lockdown weekends. Average volume of uploaded data per test unit,
broken down by the hour of the day, on weekends in the pre-lockdown time
period.

(c) Lockdown weekdays. Average volume of uploaded data per test unit, broken
down by the hour of the day, on weekdays in the lockdown time period.

(d) Lockdown weekends. Average volume of uploaded data per test unit, broken
down by the hour of the day, on weekends in the lockdown time period.

(e) 2019 vs 2020 weekdays. Average volume of uploaded data per test unit on
weekdays in March-June compared between 2019 and 2020.

(f) 2019 vs 2020 weekends. Average volume of uploaded data per test unit on
weekends in March-June compared between 2019 and 2020.

Figure 4: Graphs comparing and contrasting the average hourly upload volume of
data per test unit in various time periods. Compared with download patterns,
the lines in these graphs fluctuate more, especially on weekends. When
comparing April and May between 2019 and 2020, we see an overall increase in
uploaded data in 2020.

##  VI Hourly Data Usage Patterns

Hourly data usage patterns (upload and download) could potentially provide
insights into the behavior of fixed broadband internet users before and during
the pandemic. Some applications such as music and video streaming generally
increase the volume of downloaded data. Other applications such as VoIP, video
conferencing, or networked games influence the volume of uploaded data.

In this section, we study the average hourly data usage per test unit from
multiple perspectives and attempt to correlate the observations with changing
user behavior patterns during the lockdown. We contrast weekday and weekend
patterns, analyze the differences in uploaded and downloaded traffic volumes,
and compare with the data from the corresponding period of 2019 to eliminate
potential seasonal differences.

The analysis in this section is primarily based on the “datausage” table from
the FCC MBA raw dataset. That table keeps track of the total number of bytes
received and transmitted by all user’s devices within each hour of the day.
For hourly analysis, we needed to translate UTC timestamps to the test unit’s
local time using the unit profile dataset, as described in Section IV. All
graphs in this section were generated with measurements from approximately
3000 test units.

###  VI-A Analysis of Download Patterns

All graphs in Figure 3 show a traffic peak between 18:00 and 22:00 hours, also
known as the internet rush hour [36]. Most likely explanation for this peak is
post-workday internet multi-media consumption for entertainment purposes,
e.g., Netflix or Youtube.

Graphs in Figure 3a and 3b compare average weekday and weekend download
traffic volumes in the pre-lockdown period, i.e., until end of February 2020.
We clearly see that the overall average download volume is higher on weekdays.
However, the distribution of activity throughout the day is different for
weekdays and weekends. Between approximately 9:00 and 17:00, the weekend graph
shows a relative increase in traffic volumes compared to the weekday graph and
more variability month-to-month. This corresponds with the intuition that
users typically use fixed broadband internet more during weekends when they
are at home. The increase in variability can probably be partially explained
by seasonal effects.

Figure 3b and 3d compare weekend traffic before and during lockdown. We see
that the minimum and maximum weekend daily traffic levels are roughly the same
before and during lockdown. In March–May 2020 we see a significant increase in
daytime weekend traffic volumes (green, yellow, blue in Figure 3d) relative to
the period before lockdown. Interestingly, daytime volumes return to their
normal pre-lockdown levels in June 2020. This can be partially explained by
the fact that many restrictions in effect since April were being relaxed by
June. As a result, some users may have returned to spending their weekends
outside of the home.

When we compare lockdown weekdays with weekends in Figure 3c and 3d, we see
that the hourly weekday pattern starts to resemble weekend patterns. This
trend is particularly visible for April 2020 (yellow). This trend is also
clearly visible in Figure 3e which contrasts lockdown weekday data with
weekday data from the same period of 2019. We clearly see that 2019 and 2020
curves cluster in two separate regions during daytime from roughly 8:00 to
18:00 hours. In other words, lockdown weekday download patterns morphed into
weekend patterns. The same trend was observed by other authors in data from
different vantage points.

It is worth nothing that in Figure 3c April 2020 appears to be an outlier. All
other lockdown months appear to converge to similar traffic levels. Somewhat
lower traffic levels in May and June 2020 can be probably partially explained
by: a) a new normal, b) relaxed lockdown restrictions. The traffic levels are
highest in April because it is the first month in which most of the US states
were under a mandatory lockdown. Thus, this month represents the initial
lockdown period during which a significant proportion of users was getting
accustomed to working from home or online learning activities. The increase in
evening traffic levels is likely due to social distancing restrictions [37],
where people moved social activities to the internet.

On weekends (Figure 3f), we see a small traffic increase in March, and the
increases in April and May are substantial. Weekend June 2020 daytime traffic
levels saw a relative decrease compared with June 2019 levels, except for the
evening spike. One way to explain the difference might be that after months of
restrictions people try to spend weekends more outside their homes. This
pattern coincides with the policies related to COVID-19 in the United States:
the height of restriction was at the end of March and the beginning of April,
and some restrictions were being relaxed in the mid-late May or June in many
states [38].

We conclude that people use fixed broadband internet more and download more
data because of COVID-19 and its related policies (e.g., lockdown, quarantine,
work from home, etc.).

###  VI-B Analysis of Upload Patterns

Figure 4 shows several plots of hourly average uploaded data. As expected,
there is significantly more noise and fluctuation in these plots (particularly
weekends), compared with the download plots in Figure 3.

We note that we have no explanation for the morning spike in March 2019 shown
in Figure 4b. Unlike the corresponding download patterns, hourly upload
patterns before lockdown show little difference between weekdays and weekends,
partly due to the noise.

The average uploaded data levels April and May 2020 are larger than those in
2019, but in March and June, that is not the case (Figure 4e and 4f). Possible
reasons might be similar to those discussed earlier: the majority of US states
enforced peak lockdown restrictions at the end of March and the beginning of
April. Some of the restrictions ended in mid-late May or June in some states
[38].

On weekdays, the daytime average uploaded data levels in April, May, and June
2020 are much larger than those in the corresponding months of 2019. On
weekends, the increase is relatively smaller than on weekdays.

Figure 5: CDF that shows the ratio of average download speed experienced per
consumer between January and March of 2020.

Figure 6: CDF that shows the ratio of average upload speed experienced per
consumer between January and March of 2020.

##  VII Average Speed

Given that internet data consumption generally increased across the United
States in the patterns described in earlier sections [5, 6], an obvious
question to ask if this increase led to changes in perceived network
performance, e.g., due to increased network congestion. Knowing that the
average volume of data per test unit increased significantly, one would expect
the average internet upload or download speed to also change.

###  VII-A Average Download Speed

In order to understand how the COVID-19 outbreak impacted the average download
speed experienced by fixed broadband internet users, we calculate the ratio
between the average download speed that users experienced between January and
March 2020. Let $S_{\text{D}}$ be the download speed, we compute the average
download speed ratio as follows:

$\overline{S}_{\text{D}}\%=\frac{\overline{S}_{\text{D},\,\text{Mar}}-\overline{S}_{\text{D},\,\text{Jan}}}{\overline{S}_{\text{D},\,\text{Jan}}}\times
100$ (3)

Figure 6 shows the result in the form of a Cumulative Distributed Function
(CDF). We see that approximately 60% of the users experienced a decrease in
their average download speed from January to March 2020. However, the decrease
was not too significant, since for most of the users, the decrease was smaller
than 5%. Therefore, while a significant number of users experienced a decrease
in average download speed as other studies suggest, not many experienced a
severe decrease.

Cooper argued that although some ISPs were able to keep up with the increase
in internet consumption, some cities felt the impact of the increase in data
consumption leading users to experience a decrease in their download speed,
e.g., the download speed in New York City decreased by 24% in March [39].

###  VII-B Average Upload Speed

Similarly, we calculate the average upload speed ratio from January to March
2020. Let $S_{\text{U}}$ be the upload speed, we can calculate the average
upload speed as follows:

$\overline{S}_{\text{U}}\%=\frac{\overline{S}_{\text{U},\,\text{Mar}}-\overline{S}_{\text{U},\,\text{Jan}}}{\overline{S}_{\text{U},\,\text{Jan}}}\times
100$ (4)

Figure 6 shows the result. Approximately 70% of users saw their average upload
speed decreasing from January to March 2020. As people transitioned most of
their activities online, e.g., work from home, remote learning, and video
conferences, an increase in uploaded data volumes was expected (as Figure 6
shows) and, consequently, a decrease in the average upload speed. Similarly to
the situation observed with the download speed, while 70% of consumers
experienced a decrease in their upload speed, this decrease was also modest,
mostly less of 5%.

Figure 7: Graph comparing the 95% quantile packet loss per day for the months
January, March 2019 and January, March 2020.

Figure 8: Graph comparing the 95% quantile packet loss per hour for the months
January, March 2019 and January, March 2020.

##  VIII Packet Loss Analysis

Apart from studying data usage and connection speed patterns, we can also
examine the effects of COVID-19 restrictions on packet loss. With increasing
fixed broadband internet usage during the lockdown period, network congestion
is also likely to increase. Increasing network congestion will manifest itself
in increasing packet loss.

Each test unit performs a UDP latency measurement periodically throughout the
day. During the measurement, the test unit sends a series of UDP datagrams to
a pre-defined set of servers. Parameters such as minimum, maximum, and average
round trip time are recorded, as well as the number of lost UDP packets. The
UDP test is only performed when there is no user activity on the connection.
Thus, the results are primarily influenced by the performance of the fixed
broadband connection rather than local user traffic.

We use number the total number of sent and lost UDP datagrams to calculate
packet loss rate. To quantify the packet loss rate for one test unit, we
define the metric as:

$Packet\ Loss\,\%=\frac{\\#Failure}{\\#Failure+\\#Success}\times 100$ (5)

Previous studies suggested that the majority of the US did not experience any
degradation of network performance [18]. We thus focus on the subset of the
users that did experience network degradation and study packet loss at the
$95\%$ quantile, i.e., users at the higher end of the packet loss spectrum.

###  VIII-A Daily Packet Loss

Figure 8 shows the 95% quantile of daily packet loss over January and March
2019 and 2020. We see a significant increase in packet loss starting from mid-
March 2020. Mid-March is the period when some US states began imposing
statewide lockdown orders. In this time period, the packet loss rate grows by
40–50% compared to early March 2020, which is before the pandemic. Towards the
end of March 2020, packet loss rate is 80% higher than that before the
pandemic, implying that some network congestion might occur.

Since we do not observe similar trends in 2019, this suggests that this
increase is not due to noise and might be instead due to the additional
traffic generated by activities such as work from home, distance learning, and
video conferencing. This increase also coincides with the increase in
downloaded data we observed in metro areas in Figure 1.

###  VIII-B Hourly Packet Loss

In this section, we examine the influence of the hour of the day on packet
loss. Figure 8 depicts the 95% quantile of packet loss over the course of a
day in January, March 2019 and 2020. We found the packet loss ratio changing
over the course of the day, with the maximum around 20:00. This is consistent
with data upload and download patterns discussed earlier.

In March 2020, the packet loss rate increased steeply during daytime from 7:00
to 22:00, potentially due to the users’ growing online activities like remote
learning, work from home, etc. However, this change was more evident during
the evening. For example, we saw an around 59% growth from 18:00 to 22:00 when
comparing March to January 2020. This time period lies outside of traditional
working hours - suggesting that packet loss is more positively related to the
increased traffic due to recreational activities rather than remote working or
distance learning.

Our results are consistent with Candela et al. [40], as they found the
increase of packet loss might be affected by factors like the type of server,
the version of the Internet Protocol, and the time of the day.

##  IX Discussion

Because we were working with large datasets, $300\text{\,}\mathrm{GB}\text{/}$
of data in total from January 2019 to June 2020, one of the main challenges
that we faced was to find an application that would be able to process all
this data. We decided to use Google Cloud BigQuery and process all the data in
Google Cloud VMs. However, even in this configuration, some of the queries
that analyzed the data over long time intervals took up to ten minutes to
complete.

One of the biggest limitations in our analysis were outdated unit profile and
unit census datasets. These datasets are published by the FCC as part of the
MBA raw data. While the raw MBA datasets are published regularly, unit profile
and unit census datasets are only updated when a new annual report is
published. This is understandable since some of the information in those
datasets need to be manually cross-validated. At the time of writing, the 9th
FCC MBA report was the most recent report. That report covered September-
October 2018, hence our most recent unit profile and unit census datasets were
from 2018. Having more recent unit profile and unit census datasets would
allow us to perform more accurate demographic and geography analysis.

For the purpose of assessing users’ behavior from network traffic patterns, we
were limited by the fact that the FCC MBA datasets do not provide application
or service level information about the traffic. Thus, we were not able to
examine the relative contribution of various types of applications (VoIP,
Youtube, video conferencing) to traffic patterns. Others have examined such
data in the context of COVID-19 related lockdowns, e.g., the authors in [2].

When used in isolation, the FCC MBA dataset provides only a limited insight
into how households deal with short-term and long-term crises, e.g., natural
disasters or epidemics. The reasons include: 1) lack of application-layer
detail, i.e., what network applications are being used and when, 2) sampling
bias, 3) inability to correlate fixed broadband measurement data with similar
measurements from other related vantage points, e.g., ISPs, network operators,
or mobile networks.

For the purpose of emergency response and planning, it might be beneficial to
include a mechanism in the FCC MBA dataset to track application usage in a
privacy-respecting manner. For example, the test unit could classify uploaded
and downloaded traffic into a few broad classes such as video stream, real-
time communication, VPN, and others. On a larger scale, such information might
provide useful insight into the situation in particular geographic area, for
example, during a natural disaster.

Given that the FCC MBA program is a volunteer based program, the collected
data is likely to exhibit sampling bias. That is, the test units are more
likely to be run by users somewhat technically savvy, more likely to reside in
metro areas, or using a specific kind of broadband technology.

Finally, other network measurements exist that might provide complementary
information to the FCC MBA dataset. For example, during the pandemic, Google
published regular mobility reports based on data collected from Android
devices [41]. The authors in [19] show the level of mobility detail that could
be obtained from mobile network operator in a privacy-respecting manner. For
disaster planning and recovery purposes, it might be beneficial to find a way
to combine these and the FCC MBA dataset.

##  X Conclusion

In this paper, we studied the data collected through the FCC MBA program from
January 2019 to June 2020. Our goal was to understand the performance of fixed
broadband internet in the United States before and during the COVID-19
pandemic, and also to look for clues about changes to user behavior based on
their data usage patterns.

We uploaded the FCC MBA raw data from January 2019 to June 2020 into a Google
Cloud BigQuery database, cleaned and pre-processed it, and then analyzed using
Google Cloud Datalab (Jupyter) notebooks. We attempted to look at the data
from multiple perspectives: by geography and population size, by weekdays and
weekends, and over the course of the day.

Overall, we found a significant increase in fixed broadband data usage
consistent with the lockdown timeline. Our observations from the FCC MBA data
are more or less consistent with the observations that others made using data
from different vantage points (e.g., ISPs, IXPs, network service providers).
We reviewed and summarized some of the existing work in Section II for
context.

Broken down by geography and population density, our analysis shows traffic
spikes in both rural and metro areas. However, the rural traffic spike is
initially shorter and less pronounced. Towards June 2020, we see comparable
traffic level values in metro and rural areas. Our data suggest that users in
metro areas react to government recommendations faster and more swiftly than
users in less populated areas.

We further attempted to correlate local ordinances with county-level usage
patterns for Los Angeles county. There, we saw the first traffic spike on
March 14, a day after many schools in the county shutdown. Another spike came
after the March 16 White House stay-at-home order. We note that our geography
and demographic analysis is partially limited by older (2018) unit profile and
unit census datasets and fairly small number of test units at the county
level.

Looking at weekday and weekend traffic patterns, we saw a significant increase
in daytime weekend traffic from March to May 2020. By June the levels return
to their normal pre-lockdown levels, suggesting that users might have complied
with stay-at-home recommendations only initially. Consistent with others, we
see the weekday daytime traffic pattern changing into the weekend pattern,
with a significant increase of uploaded and downloaded data during the day. We
attribute most of that increase to distance learning and work from home.

Our analysis of the average hourly downloaded and uploaded data indicates that
the policies related to COVID-19, such as lockdown, stay-at-home order, work
from home recommendation, etc., increase the usage of internet in the United
States on both weekdays and weekends.

Our average speed analysis uncovered a slight 5% decrease in average speed for
about 60-70% of users. We also found a significant almost 50% increase in
packet loss for some users starting mid-March 2020.

###  X-A Future Work

The results presented in this report present various potential future avenues
for exploration.

In our population demographics analysis, we had used county-level populations
as a measure of population density. Since the unit census dataset records a
census block group (a smaller census unit) for each test unit, there is a more
accurate way to calculate population density. It might be possible to obtain a
boundary polygon for each census block group, e.g., from an existing ArcGIS
database, and then calculate population density from the block group’s area
and population.

In addition, having a more recent unit profile and unit census data could help
us increase the test unit sample size used in our analysis. Since most of our
analysis does not rely on the cross-validated ISP information included in the
unit profile dataset, obtaining a partial (unpublished) unit profile data from
the FCC would help us improve our analysis.

For user behavior analysis, the biggest limitation of our approach is that we
do not know how specific applications contribute to the overall traffic for
each test unit, e.g., Netflix, Youtube, Zoom, etc. Incorporating other
datasets with this information obtained from other vantage points could help
us better understand user behavior from network traffic patterns.

Interesting insights into user behavior during lockdown might be perhaps
obtained by correlating fixed and mobile broadband internet usage. These two
datasets, when used together, would present a more complete picture of user
behavior.

We have only analyzed a subset of the metrics provided by the FCC MBA dataset.
Our study could be expanded by analyzing metrics such as latency, VoIP and
media streaming performance, DNS performance, and others. Additionally,
studying network performance by broadband connection type (cable, fiber,
satellite) might provide additional insights.

## Acknowledgment

Jessica De Oliveira Moreira was funded by Craig Newmark Philanthropies while
working on the project. She would also like to thank to the DIMACS REU Summer
2020 Research program and the Barnard Computer Science department for
additional support.

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