Solar Superstorms: Planning for an Internet Apocalypse
Sangeetha Abdu Jyothi

University of California, Irvine and VMware Researchsangeetha.aj@uci.edu

ABSTRACTOne of the greatest dangers facing the Internet with the potential
for global impact is a powerful solar superstorm. Although humans
are protected from these storms by the earth’s magnetic �eld andatmosphere, they can cause signi�cant damage to man-made infrastructure. The scienti�c community is generally aware of thisthreat with modeling e�orts and precautionary measures being
taken, particularly in the context of power grids [41, 43]. However,the networking community has largely overlooked this risk during
the design of the network topology and geo-distributed systems
such as DNS and data centers.
A Coronal Mass Ejection (CME), popularly known as solar storm,
is a directional ejection of a large mass of highly magnetized particles from the sun. When the earth is in the direct path of a CME,
these magnetized and charged solar particles will interact withthe earth’s magnetic �eld and produce several e�ects. In addition
to spectacular auroral displays, they produce Geomagnetically Induced Currents (GIC) on the earth’s surface through electromagnetic induction. Based on the strength of the CME, in extreme cases,
GIC has the potential to enter and damage long-distance cables that
constitute the backbone of the Internet.
The largest solar events on record occurred in 1859 and 1921, long
before the advent of modern technology. They triggered extensive
power outages and caused signi�cant damage to the communication network of the day, the telegraph network. The probability ofoccurrence of extreme space weather events that directly impact
the earth is estimated to be 1.6% to 12% per decade [42, 65]. More
importantly, the sun was in a period of low activity in the past three
decades [61] from which it is slowly emerging. Since this low phaseof solar activity coincided with the rapid growth of technology on
the earth, we have a limited understanding of whether the current
infrastructure is resilient against powerful CMEs.
In this paper, we analyze the threat posed by solar superstorms
to the Internet infrastructure and the steps to be taken to mitigate
its e�ects. First, we ask a key question: is the threat signi�cant, andshould we factor this in Internet topology design and infrastructure
deployment (§ 2)? Second, we study the impact of solar storms on
key building blocks of the Internet infrastructure — long-haul landand submarine cables (§ 3). Third, using real-world datasets anda wide range of failure models, we quantify the impact of solar
superstorms on the Internet infrastructure (§ 4). Finally, we lay out
steps to manage the perils associated with solar superstorms (§ 5).
Long-distance �ber cables and communication satellites are susceptible to damage from solar storms through induced currents anddirect exposure, respectively (§ 3). In cables, the optical �ber itself
is immune to GIC. However, long-haul cables have repeaters toboost the optical signals spaced at intervals of 50 150 km which
are powered using a conductor. These repeaters are vulnerable to
GIC-induced failures, which can lead to the cable being unusable.
GPS and communication satellites which are directly exposed tosolar storms will su�er from lost connectivity during the event,Black swan events are hard-to-predict rare events that can signi�cantly alter the course of our lives. The Internet has played a keyrole in helping us deal with the coronavirus pandemic, a recent
black swan event. However, Internet researchers and operators aremostly blind to another black swan event that poses a direct threat
to Internet infrastructure. In this paper, we investigate the impact
of solar superstorms that can potentially cause large-scale Internet
outages covering the entire globe and lasting several months. We
discuss the challenges posed by such activity and currently available mitigation techniques. Using real-world datasets, we analyze
the robustness of the current Internet infrastructure and show thatsubmarine cables are at greater risk of failure compared to land
cables. Moreover, the US has a higher risk for disconnection compared to Asia. Finally, we lay out steps for improving the Internet’s
resiliency.

CCS CONCEPTS• Networks → Network reliability; Network structure;

KEYWORDS
Internet Resilience, Internet Topology, Solar storms
ACM Reference Format:
Sangeetha Abdu Jyothi. 2021. Solar Superstorms: Planning for an Internet
Apocalypse. In ACM SIGCOMM 2021 Conference (SIGCOMM ’21), August
23–27, 2021, Virtual Event, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3452296.3472916

1 INTRODUCTION

What will happen if there is a global Internet collapse? A disruption
lasting even a few minutes can lead to huge losses for service
providers and damages in cyber-physical systems. The economic
impact of an Internet disruption for a day in the US is estimated to
be over $7 billion [1]. What if the network remains non-functional
for days or even months? This is the worst-case scenario, which,fortunately, we have never encountered in recent history. Threats to
the Internet range from man-made cyber attacks to natural disasters
such as earthquakes. The Internet is also a�ected by black swan
events such as the Covid-19, which profoundly alter human lives
and, in turn, our Internet usage. However, the in�uence of theseindirect threats on the Internet is only secondary, with the worstcase impact often limited to reduced speeds.

This work is licensed under a Creative Commons AttributionNonCommercial-ShareAlike International 4.0 License.
SIGCOMM ’21, August 23–27, 2021, Virtual Event, USA© 2021 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-8383-7/21/08.
https://doi.org/10.1145/3452296.3472916

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Sangeetha Abdu Jyothi

2

potential damage to electronic components, and in the worst case,orbital decay and reentry to earth (particularly in low earth orbit
satellites such as StarLink [14]).
In order to study the impact of CMEs on terrestrial networks, weuse a comprehensive set of Internet topology datasets, including
submarine and land cables, DNS root servers, IXPs, Internet routers,
etc. Since accurate modeling of repeater failures is not available,
we employ a broad range of failure models derived based on GIC
characteristics.
Our experiments provide several interesting insights regarding
the Internet topology and its vulnerabilities. First, the topology isskewed with respect to Internet user distribution. There is a higher
concentration of infrastructure elements on higher latitudes thatare more vulnerable to solar superstorms. Second, submarine cables are more vulnerable than land cables, primarily due to theirlarger lengths. Third, di�erent regions will be impacted di�erently.
The US is highly susceptible to disconnection from Europe. Europe is in a vulnerable location but is more resilient due to thepresence of a larger number of shorter cables. Asia has relatively
high resilience with Singapore acting as a hub with connections
to several countries. Finally, we analyze the impact on various Internet systems. DNS root servers are less vulnerable since they arehighly geo-distributed. Google data centers have better resilience
than Facebook’s. A large fraction of Autonomous Systems have apresence in the higher latitudes, but a majority of them are geographically restricted to a smaller area.
Although the highest priority system for recovery during a solar
event will be the power grid, the Internet is also a critical infrastructure necessary for disaster management. While this paper focuseson the vulnerabilities of the Internet infrastructure alone, a discussion on the interdependence with power grids and associatedchallenges are presented in § 5.

MOTIVATION: A REAL THREAT

In this section, we present a discussion on threats posed by solar
activity and the likelihood of extreme solar events that can a�ect
the earth.

2.1

Solar �ares and CMEs

Solar activity waxes and wanes in cycles, with a period lengthof approximately 11 years [23]. During solar maxima, there is anincrease in the frequency of two solar phenomena, solar �ares andCoronal Mass Ejections (CMEs), both caused by contortions in the
sun’s magnetic �elds [35].
Solar �ares involve large amounts of emitted energy as electromagnetic radiation. Although �ares can reach earth in 8 minutes,
they a�ect only the upper layers of the atmosphere, particularly
the ionosphere, causing disruptions to satellite communication andGPS. Solar �ares do not pose any threat to terrestrial communication or other infrastructure.
A Coronal Mass Ejection (CME) involves the emission of electrically charged solar matter and accompanying magnetic �eld into
space. It is typically highly directional. This cloud of magnetizedparticles can take 13 hours to �ve days to travel to the earth. They
cannot penetrate the atmosphere and a�ect humans directly. However, they will interact with the earth’s magnetic �eld and induce
strong electric currents on the earth’s surface that can disrupt andeven destroy various human technologies. This will occur only if
the earth happens to be on the path of a CME.

2.2

Past CME events

The �rst recorded CME with a major impact on the earth is the Carrington event (Sep 1, 1859) [21]. This cataclysmic CME reached theearth in just 17.6 hours owing to its very high speed. The communication network of the day, the telegraph network, su�ered fromequipment �res, and several operators experienced electric shocks.
This caused large-scale telegraph outages in North America andEurope. Even when power was disconnected, telegraph messages
could be sent with the current generated by the CME. A recent
study [50] which analyzed the risks posed by a Carrington-scale
event to the US power grid today found that 20 40 million people
could be without power for up to 2 years, and the total economic
cost will be 0.6 2.6 trillion USD. To the best of our knowledge,
there are no existing studies on the risks posed to the Internet
infrastructure by such an event.
The largest geomagnetic storm of the 20C⌘ century, which occurred in May 1921, named the New York Railroad superstorm
based on its impact on NY telegraph and railroad systems, also
caused widespread damage across the globe [47]. Note that thestrongest CME of the past century happened before widespreadelectri�cation. A smaller-scale CME had caused the collapse of thepower grid in the entire province of Quebec, Canada, and over 200
grid problems at various locations in the US in 1989 [59]. However,this was only a moderate-scale CME.
A CME of Carrington-scale missed the earth by merely a week
in July 2012 [62]. Fortunately, given the highly directional natureof CMEs, they can cause signi�cant damage only when the earth isin the direct path.

In summary, we make the following contributions:• We present the �rst study that analyzes threats to the Internet
infrastructure posed by a high-risk event: solar superstorms.
• We identify several vulnerabilities in the design of current Internet topology and associated geo-distributed infrastructure such
as DNS and Autonomous Systems.
• We show that the Internet infrastructure distribution is skewed
with respect to the user population. Internet infrastructure components are concentrated in higher latitudes that are susceptible
to solar events.
• We investigate the impact of Geomagnetically Induced Currents
on various infrastructure components and show that submarine
cables are at the highest risk of damage.
• We demonstrate that the potential impact of solar superstorms
on di�erent regions varies widely. The US is highly vulnerable
to disconnection compared to Asian countries.
• We discuss several open questions on improving Internet resiliency, including how to factor in solar superstorms during thedesign of Internet topology and other Internet sub-systems.
This paper does not raise any ethical concerns.

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Can we predict the next large event?

3

Similar to other natural phenomena such as earthquakes and thecollapse of a star into a black hole, solar activity is extremely di�cultto predict. To make matters worse, unlike earthquakes, we have
limited data on intense solar phenomena that impact the earth
because they are rarer and more di�cult to study. Although it
is impossible to forecast the exact occurrence of a catastrophic
solar event and prediction of such events continues to be one of
the hardest challenges in astrophysics, scientists have developed
several models based on past observations.
Frequency estimates based on limited data for a direct impact
currently range from 2.6 to 5.2 per century [16, 17, 51, 52]. There
are also several studies assessing the probability of occurrence ofa Carrington-scale event. Current estimates range from 1.6% [42]to 12% [65] probability of occurrence per decade for a large-scale
event (note that the probability of occurrence per decade of a oncein-a-100-years event is 9%, assuming a Bernoulli distribution where
events are independent). Today, there are several models with various knobs to capture the behavior of solar cycles. But the sensitivityof these knobs and the actual behavior of the sun remains largely
elusive, with no clear winner across models. However, there is another factor that increases the risk of solar storms in the near term
(next couple of decades).
The frequency of CMEs is not uniform across solar maxima.
In addition to the 11-year cycle, solar activity also goes througha longer-term cycle in approximately 80 100 years called the
Gleissberg cycle [33, 61]. This cycle causes the frequency of highimpact events like CMEs to vary by a factor of 4 across solar maxima [51]. The most recent solar cycles, cycle 23 (1996-2008) andcycle 24 (2008-2020), are a part of an extended minimum in thecurrent Gleissberg cycle [30, 31]. In other words, modern technological advancement coincided with a period of weak solar
activity and the sun is expected to become more active in thenear future. Hence, the current Internet infrastructure has notbeen stress-tested by strong solar events.
Early predictions for the current solar cycle, which began in
2020, ranged from weak [19, 71] (a part of the current Gleissberg
minimum) to moderately strong [28, 46, 67]. However, a recent
study from November 2020 [53] suggested that this cycle has thepotential to be one of the strongest on record. Recent estimates forthe number of sunspots at the peak of this cycle are between 210 and
260 (a very high value) [37, 53]. In contrast, the previous cycle that
ended in 2019 had a peak sunspot number of 116. Since CMEs often
originate in magnetically active regions near sunspots, a larger
number of sunspots will increase the probability of a powerful
CME. If this estimate [53] proves accurate, it will also signi�cantly
increase the probability of a large-scale event in this decade. The
actual strength of this cycle will be evident only later in the decadeas the solar cycle progresses.
In the 20C⌘ century, the Gleissberg minimum point was in 1910 [31]and the largest CME of the century occurred a decade later in1921 [47]. The past 2 3 solar cycles, which coincided with thebirth and growth of the Internet were very weak. Given that a strongsolar cycle that can produce a Carrington-scale event can occur inthe next couple of decades, we need to prepare our infrastructure
now for a potential catastrophic event.

IMPACT ON NETWORKS

Having established that solar superstorms are a real threat with asigni�cant probability of occurrence in the near- and long-term, inthis section, we discuss its impact on networks. We provide a brief
overview of how CMEs produce geomagnetically induced currentson the earth’s surface and how they a�ect Internet cables. We alsobrie�y mention the e�ects on satellite communication. However,the focus of this paper is the impact on terrestrial communication
networks, which carry the majority of the Internet tra�c.

3.1

Geomagnetically Induced Current

CMEs produce variations in the earth’s magnetic �eld, which inturn induce geoelectric �elds on the earth’s conducting surface (i.e.,
land and ocean �oor). These spatiotemporally varying electric �elds
are responsible for the generation of Geomagnetically Induced Currents (GIC) [32, 64], as high as 100-130 Amps [58], that can �ow
through any extended ground-based conductive systems such as
power grids, networking cables, etc. This electromagnetically induced current enters/exits long-distance conductors from grounded
neutral, causing destruction of electrical equipment such as transformers/repeaters and, in turn, large-scale power outages/Internet
outages spanning many states or even countries. The amplitude of
GIC depends on a variety of factors, such as the time derivative of
the geomagnetic �eld and the resistivity of the earth’s crust andupper mantle.
Several factors in�uence the strength of GIC. (i) Conductor length:
GIC is primarily induced in “long conductors” since the current is
proportional to the area of the loop formed by the two grounds andthe cable [54]. Hence, power grids [41, 43], oil and gas pipelines [72],networking cables, etc. are most vulnerable. Geographically localized infrastructure such as data centers can be protected using
Transient Voltage Surge Suppression (TVSS) devices which are relatively inexpensive (~$1000s). (ii) Latitude: Higher latitudes are at asigni�cantly higher risk [63, 68, 69] (similar to other solar e�ects
such as auroras). During the medium-scale 1989 event, the magnitude of the induced electric �eld dropped by an order of magnitude
below 40> # [63]. During the Carrington event, estimates show thatstrong �elds extended as low as 20> # [63] (limited measurements
available from 1859). Recent studies show that GIC of small magnitudes can occur at the equator [22, 68, 75]. But the strength ofvariations in the �eld in equatorial regions is signi�cantly lower
than that in higher latitudes [22, 68, 75]. (iii) Geographic spread:
Since GIC is caused by changes to the earth’s magnetic �eld, ita�ects wide areas and is not restricted to the portion of the earthfacing the sun. (iv) Orientation of conductor: Since CME-induced
�uctuations do not have a directional preference (e.g., North-Southvs. East-West), conductors along di�erent orientations on earth areat equal risk [32].
Note that seawater has high conductivity [26]. The presence ofhighly conductive seawater over more resistive rocks increases thetotal conductance of the surface layer [27]. Hence, the ocean does
not reduce the impact of GIC but increases it. For example, a study
that modeled the geoelectric �elds and potential GIC impact around
New Zealand reported conductance in the range 1-500 S on landand 100-24, 000 S in the ocean surrounding New Zealand [27]. A
higher conductance implies that a higher GIC could be induced.

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3.2

Sangeetha Abdu Jyothi

Impact on Long-Distance Cables

During the moderate storm of March 1989 [59] (one-tenth thestrength of 1921 storm), considerable potential variations were
measured on the only long-distance submarine cable of the time,
the AT&T cable between New Jersey, US and the UK [56]. Since
the variations were of moderate magnitude, the PFE at landing
stations could control the impact. However, the cost for designingand deploying PFEs that are able to handle the highest possible
variations is exorbitant and hence, not practical [45]. A TVSS device
at the landing point cannot protect repeaters along the length ofthe cable.
Modeling the impact of GIC on repeaters and cables is an extremely di�cult task. During a CME, induced GIC at a location will
depend on the strength of the induced electric �eld, its direction,
ground/crust composition, and a variety of other factors. Moreover, while long-distance cable endpoints are well-known, we have
limited information about intermediate grounding points and theextent of GIC possible between them based on terrestrial characteristics. In short, a detailed analysis of the impact of CMEs on
long-distance cables is hindered by the limited availability of data
as well as the lack of tools and techniques that facilitate analysis at
the intersection of astrophysics, electrical engineering, and opticalengineering.

3.2.1 Understanding the vulnerabilities. Long-distance land andsubmarine cables carry signals in optical �bers. The �ber in thecable is immune to GIC, unlike the previous generation of coaxial
cables, since it carries light and not electric current. However, longhaul cables that stretch hundreds or thousands of kilometers also
have an accompanying conductor that connects repeaters in series
along the length of cables called the power feeding line [4]. This
conductor is susceptible to GIC [5, 49].
Power Feeding Equipments (PFEs), located in landing stations
at the ends of the cable, power the repeaters which are connected
in series via the power feeding line with a 1.1 Ampere regulated
current. The resistance of the power feeding line is approximately
0.8 ⌦/km. However, the actual voltage requirement is in�uenced
by several factors, including earth potential di�erence at either
end of the cable, the number of spare repeaters in the cable, etc.
Considering these factors, a 10 Gbps 96-wave 9000 km long cabletypically requires a power feeding voltage of about 11 kV andapproximately 130 repeaters connected in series [76]. In practical
deployments, inter-repeater distance vary from 50 to 150 km [48,66].
Note that the repeaters are designed to operate at ~1A current [73,76]. However, as discussed in § 3.1, GIC during strong solar events
can be as high as 100 130 Amperes. This is ~100⇥ more than theoperational range of these repeaters. Thus, in the event of a solar
superstorm, repeaters are susceptible to damage from GIC. Moreover,even a single repeater failure can leave all parallel �bers in the cableunusable due to weak signal strength or disruption of power.

In this paper, we take the �rst step towards bridging the gap in
our understanding of the impact of CMEs on terrestrial communication infrastructure using an extensive set of infrastructure datasets
and a broad range of failure models. To the best of our knowledge,
this is the �rst e�ort to bring together various pieces required tounderstand this threat in the context of the Internet. Note that
more sophisticated models, particularly for repeater failures, can beplugged into our analyses in the future when they become available.
This paper uses a wide range of probabilistic models to study the
space since accurate modeling is not available. Real-world datasetsand models are used in all contexts where feasible and available.

3.2.2 Recovery challenges. The speci�ed lifetime of repeatersin submarine cables is 25 years [24]. Once deployed under theocean, they are typically highly resilient unless the cable is damagedby human interference. This is a design requirement since thereplacement/repair of repeaters or parts of the cable is expensive.
Commonly, underwater cable damages are localized, and typicalcauses are �shing vessels, ship anchors, or earthquakes. Whendamage occurs, the location of the damage is �rst identi�ed using
tests from the landing sites, and then a cable ship is sent to thelocation for repair. This repair process can take days to weeks for asingle point of damage on the cable.
The current deployment of submarine cables has never beenstress-tested under a strong solar event. Due to the lack of realworld data on GIC e�ects on repeaters, the potential extent ofdamage (the number of repeaters that could be destroyed) and thetime required to repair signi�cant portions of a cable are unknown.
However, the extent of damage is not dependent on the distance
between the repeaters. It depends on the distance between the
ground connections. GIC enters and exits the power feeding line
at the points where the conductor is grounded, even when thecable is not powered. The potential di�erence between these earth
connections determines the strength of GIC entering the cable.
A short cable (<50 km) without any repeaters does not require aground connection. However, longer cables are grounded at several
intermediate points, at intervals of 100s to 1000s of kilometers.
For example, Google’s Equiano cable connecting South Africa to
Portugal has nine branching units [9] which are connected to thesea earth.

3.3

Impact on Satellites

Communication satellites are among the severely a�ected systems.
The damages are not caused by GIC but due to direct exposure tohighly charged particles in CMEs. These particles do not reach theearth’s surface since they are mostly blocked by the atmosphere.
Threats to communication satellites include damage to electronic
components and extra drag on the satellite, particularly in lowearth orbit systems such as Starlink [14], that can cause orbital
decay and uncontrolled reentry to earth [39].
Thus, both surface-based and satellite-based communication systems are under high risk of collapse if a Carrington-scale event
occurs again.

4

ANALYZING THE IMPACT

To better understand the risk, we analyze various infrastructurecomponents that constitute the current Internet topology. In addition to the threats posed to physical infrastructure, we also evaluate
the implications for software building blocks of the Internet. Alldatasets except the private ITU land cables and the code used for experiments in this paper are available at https://github.com/NetSAILUCI/Internet-Resilience.

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exact latitude and longitude information are not available for thisdataset. But nodes labeled with location names and the links areavailable. A snapshot of submarine cables, long-haul �ber, and IXP
locations from the ITU website [10] is shown in Figure 1.

(a) Americas

4.1.4 CAIDA dataset. We use the CAIDA Internet Topology
Data Kit to analyze the distribution of routers across the globe aswell as the extent of the spread of Autonomous Systems (AS). Thisdataset contains latitude and longitude information of 46, 032, 818
Internet routers. This dataset also contains router to AS mappingfor 46, 014, 869 routers across 61, 448 Autonomous Systems(ASes),
which allows us to study the geographic spread of these ASes. We
use the topology derived using MIDAR and i�nder. This topology
has the highest con�dence aliases with very low false positives.

(b) Africa, Eurasia, and Oceania

Figure 1: IXPs, long-distance transmission links on land, andsubmarine cables [10].

4.1.5 DNS root servers. DNS root server locations of 1076 instances across 13 root servers are obtained from the root server
directory [3].
4.1.6 IXP dataset. The IXP dataset obtained from the PCH Internet Exchange directory [13] contains 1026 locations across theglobe, including their coordinate information.
4.1.7 Data center map. A visual snapshot of data centers acrossthe globe was obtained from Data Center Map [2] (Figure 2). We
also analyze data center locations of large content providers such
as Google [6], Facebook [38], etc.
4.1.8 Population dataset. To estimate the fraction of population a�ected at each latitude, we use the gridded population data(with population per cell of length and breadth 1> ) from NASA
Socioeconomic Data and Applications Center [70].

Figure 2: Public data centers and colocation centers [2].

4.1

Datasets

4.2

In this section, we give a detailed overview of datasets used in thephysical infrastructure analysis.

Infrastructure Distribution

Due to the location dependence of solar storm impact, understanding the physical infrastructure distribution is the �rst step towards
understanding infrastructure vulnerabilities.

4.1.1 Submarine cable map. The submarine cable map [15] consists of 470 cables which interconnect 1241 landing points (nodes)
across the globe with latitude/longitude information of the landing
points. Each cable has multiple branches and thus interconnects
several cities. The longest cable has a length of 39, 000 km.

4.2.1 Methodology. As discussed in § 3, long �ber cables with
conductors for powering repeaters are at a signi�cantly high risk.
There are two key factors that a�ect a cable’s risk of damage under
GIC: location of its end points and its length.
First, locations above 40> # and below 40> ( are more a�ected bysolar superstorms (we use the conservative threshold of 40> statedin [63]. Various studies consider di�erent thresholds in the range
40 ± 10> ). Hence, we evaluate the impact of solar superstorms on
the Internet by �rst analyzing the distribution of network topology
and related systems such as DNS, IXPs, etc. across various latitudes.
Second, longer cables are at a higher risk (§ 3.2). Hence, we analyze
the link lengths across various datasets to understand the risks
faced by both land and submarine cables.

4.1.2 Intertubes dataset. We rely on the Intertubes [29] datasetwith the US long-haul �ber endpoints for analysis of land transmission lines in the US.
4.1.3 ITU dataset. The TIES version of the ITU transmission
map consists of land and submarine communication network information. This map is built using data from several sources ranging
from direct information obtained from operators to secondary maps
built by organizations for larger regions or countries. Since submarine cables are considered as a separate dataset, we use only
land cables from ITU in our analysis. Moreover, while the original
dataset contains �ber and microwave links, we restrict our analysis
to �ber links. The ITU dataset contains both long-haul and shorthaul links. Note that we have not removed the Intertubes dataset
from the ITU dataset to prevent unnecessary network partitioning
in the bigger dataset. The ITU dataset contains 11, 737 network
links from across the globe that interconnect 11, 314 nodes. The

4.2.2 Evaluation. In Figure 3, we plot the probability density
function of submarine endpoints and world human population (eachwith densities calculated over 2> intervals). Although a signi�cant
fraction of the population in the northern hemisphere is below
the 40C⌘ parallel N, there is a higher concentration of submarine
endpoints at higher latitudes.

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0

180E

Latitude45

Percentage above threshold

180W
90

Sangeetha Abdu Jyothi

40 N

0
40 S

-45
-90

Population
Submarine endpoints
0

2

4

6

8

10

100

Submarine endpoints
One-hop endpoints
Intertubes endpoints
Population

80
60
40
20
0

90

80

70

Probability Density Function (%)

60

50

40

30

20

10

0

20

10

0

|Latitude| threshold(a) Long-Distance Cable endpoints

Percentage above threshold

Figure 3: PDF of population and submarine cable end points
with respect to latitude.
While smaller countries have limited �exibility regarding thelocation of their submarine endpoints due to their tight geographical boundaries, we observe that there is room for improvement,
particularly in larger nations like the US. While submarine cables
between the US and Asia are more uniformly distributed along thewest coast from Seattle to Southern California, there is a higher
concentration of cables between the North East and the northernparts of Europe. There is only a single cable connecting Florida
with Portugal and Spain in southern Europe (below 40> # ).
We analyze the location distribution of other components in
the Internet ecosystem (data centers, DNS root servers, IXPs) andobserve a similar pattern of higher density at higher latitudes. The
distribution of public data centers and colocation centers are shownin Figure 2 which follows the same pattern. In Figure 4, we showthat 31% of submarine endpoints, 40% of Intertubes endpoints, 43%
of IXPs, 38% of Internet routers, and 39% of DNS root servers arelocated above 40> . Moreover, another 14% of submarine endpoints
have a direct link to these nodes, putting these locations at riskof GIC induced currents as well. However, only 16% of the world
population is in this region. 1 2
We also evaluate the average cable length in the US long-haul�ber network, the global ITU land �ber network, and the global
submarine cable network. The US long-haul �ber dataset [29] only
provides approximate node locations and link information. Since
these cables are known to be located adjacent to the US road system [29], we estimate the link length as the driving distance between
the endpoints using Google maps API. From the publicly available

100

Internet routers
IXPs
DNS root servers
Population

80
60
40
20
0

90

80

70

60

50

40

30

|Latitude| threshold(b) Other infrastructure

Figure 4: Distribution of network elements and population as percentage above latitude thresholds. One-hop endpoints are submarine endpoints within a direct connection
to points above the threshold.
submarine dataset [15], we use 441 out of 470 cables for which
length information is available.
In Figure 5, we observe that cable lengths are an order of magnitude higher in the submarine network (775 km median, 28000 km
99C⌘ percentile, and 39000 km maximum). A large fraction of land
cables are not vulnerable to GIC since they are shorter than 150
km and hence, do not need repeaters. Due to the relatively large
link lengths and presence of repeaters, submarine cables are more
vulnerable to failures. They are also more di�cult to repair [25].

1 Note that since the Internet user population in developing countries grew rapidly inthe past two decades and the Internet infrastructure deployment has not advanced at
the same pace, observations from past work such as the Internet infrastructure being
located predominantly where the users reside [44] are not valid today.
2 While the Internet user distribution is not the same as the population distribution,these are very similar, and our conclusion regarding the distribution of Internet infrastructure in relation to users holds. The di�erence in percentage points between the
population of a continent and Internet users in a continent as a fraction of the world
is at most 5.5% [8]. For e.g., Asia has 55% of the world’s population and 52% of theworld’s Internet users (di�erence of 3% in Asia, the largest di�erence of 5.5% in Africa).
While Internet user statistics based on latitude are not available, using the highest
rate of Internet penetration above 40> and recent data on total Internet users [8], an
upper bound on the percentage of Internet users in this region is 24% of the world
population. In short, the large skew of Internet resources remains true even when we
restrict the comparison to Internet users and not the total population.

4.3

Infrastructure Resilience

The impact of a solar event extends well beyond the event basedon the extent of damage caused and the time needed for recovery. In this section, we report results on preliminary experimentscharacterizing the vulnerability of long-haul networks.
4.3.1 Methodology. We evaluate the resilience of long-distancecables using a broad range of repeater failure models. In practical deployments, inter-repeater distance range from 50 to 150:< [48, 66].

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4.3.3 Non-uniform repeater failure probability. In practice, the magnitude and the reach of GIC are dependent on thelocation of the infrastructure. Higher latitudes are at a greater risk(§ 3.1). The risk is lower but not zero at lower latitudes as well.
However, the strength of the induced currents will be much lower
at lower latitudes. Hence, we conduct repeater failure analysis using
two realistic failure models with latitude-dependent probabilities
of failure.
Repeaters in a cable are assigned a probability of failure basedon the highest latitude (!) endpoint of the cable. The three levels of
failure are demarcated by latitudes 40> and 60> (! > 60, 40 < ! < 60,
and ! < 40). We estimate loss of connectivity under two states, ( 1
and ( 2 , with high ([1, 0.1, 0.01]) and low ([0.1, 0.01, 0.001]) repeater
failure probabilities across three latitude levels.
In Figure 8, we show cable and node failures with non-uniform
repeater failure probabilities across latitudes. We observe that link
and node failures are an order of magnitude higher in the submarine
network under both high and low failure rate states. 43% cables lose
connectivity in ( 1 with failure probabilities [1, 0.1, 0.01] compared
to 80% under uniform failure probability of 1. Even under the low
failure scenario (( 2 ), around 10% of nodes and cables are vulnerable
in the submarine network. However, these probabilities are negligible in the US land network. This analysis was not conducted forthe ITU land network because the exact latitude and longitude oflocations are not available. However, given that the ITU network
performs better than the US land network under all scenarios in theuniform failure probability analysis, we can consider the US land
network performance as an upper bound for the ITU land network
performance.
In short, submarine cables are at a signi�cantly higher risk than�ber cable networks on land.

1

CDF

0.8
0.6
0.4
ITU (global, land)Intertubes (US, land)
Submarine (global)

0.2
0

1

10

100

1000

10000

Length (km)Figure 5: Cable lengths of submarine cables (across the globe), longhaul �ber on land (US only) and ITU land cables (across the globe).
We evaluate the impact of repeater failure on connectivity at threevalues of inter-repeater distances: 50 km, 100 km, and 150 km on
the US land network, the ITU global land network, and the global
submarine network. At a repeater distance of 150 km, 258 out of
542 US land cables, 8443 out of 11, 737 ITU global land cables, and82 out of 441 submarine cables do not need a repeater. At 150:<,
the average number of repeaters per cable is 0.63, 1.7, and 22.3 inthe ITU land network, the US land network, and the submarine
network, respectively.
The extent of failure of repeaters will vary based on their location,
their type, the strength of the solar storm, etc. Since the actual
probability of failure of repeaters is not known, we conduct tests
using a broad range of models. We assume repeaters are located atconstant intervals and have the same probability of failure on eachcable. If at least one repeater fails, we mark the cable as dead. In bothland and submarine networks, we assume a node is unreachable
when all its connected links have failed.

4.3.4 Connectivity analysis at country-scale. The cable- andnode-level analysis presented in the previous section does not give
us a comprehensive view of how various countries are a�ected. To
�ll this gap in our understanding, next, we analyze the connectivity
at the scale of countries across various uniform and non-uniform
probabilities of repeater failures. We investigate city-, region- andcountry-level connectivity. Key observations based on the more
realistic non-uniform probability scenarios, ( 1 (high failure) and ( 2(low failure), are presented below at the scale of countries.

4.3.2 Uniform repeater failure probability. In the uniform
failure probability analysis, all repeaters have the same probabilityof failure. This helps us identify vulnerabilities across networks ata coarse granularity.
In Figures 6 and 7, we compare the extent of failure of links
and nodes in the submarine cable network, the US long-haul network, and the ITU global �ber network at various probabilities ofrepeater failures. For each value of the probability of failure, we
repeat the experiment 10 times for each network and plot the mean
and the standard deviation. We observe that even a 1% failure rate
for repeaters at an inter-repeater distance of 150 km can lead tothe failure of 14.9% submarine cables leaving 11.7% submarine endpoints unreachable. At the same probability, only 1.7% of US long
haul �ber cables and 0.6% of ITU �ber cables fail, with a very small
fraction of nodes unreachable (0.07% and 0.1% respectively). Duringcatastrophic events with a large probability of repeater failure, at
an inter-repeater distance of 150 km, nearly 80% of undersea cables
will be a�ected, leaving an equal fraction of endpoints unreachable,
whereas 52% of cables and 17% of nodes in the US land network area�ected.

US: Under low failures (( 2 ), on the West coast, while most cables
connected to Oregon fail, connectivity from California to Hawaii,Japan, Hong Kong, Mexico, Costa Rica, etc. are una�ected. Under similar conditions on the East coast, connectivity between the
North East (and Canada) to Europe fail completely with a probability of 0.8. With a probability of 0.2, connectivity of all but one
cable is lost. Connections from Florida to Brazil, the Bahamas, etc.
are not a�ected under the low failure scenario. Under high failures (( 1 ), on the West coast, all long-distance connectivity is lost
except for one cable interconnecting Southern California with Indonesia/Hawaii/Micronesia/ Philippines. On the East coast, only
long-distance cables from Florida to the Caribbean, Virgin Islands,
Columbia, etc. remain operational. US-Europe connectivity is lostwith a probability of 1.0.
While Hawaii loses its connectivity to Australia, it remains connected to the continental US and Asia even under high failures (( 1 ).

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60
40
20

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1

100

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ITU

Cables failed (%)

80

Cables failed (%)

Cables failed (%)

100

Sangeetha Abdu Jyothi

0.01

0.1

80

Submarine
Intertubes
ITU

60
40
20
0
0.001

1

0.01

0.1

Probability of repeater failure

Probability of repeater failure

Probability of repeater failure(a) Repeater distance 50 km(b) Repeater distance 100 km(c) Repeater distance 150 km

1Figure 6: The impact of repeater failure on cables under uniform probability of failure of repeaters. A cable fails if at least oneof its repeaters fails. Error bars denote standard deviation computed over 10 trials.

100

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Intertubes
ITU

60
40
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Nodes Unreachable (%)

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Nodes Unreachable (%)

100

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ITU

60
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0.1

Probability of repeater failure

Probability of repeater failure

Probability of repeater failure(a) Repeater distance 50 km(b) Repeater distance 100 km(c) Repeater distance 150 km

1Figure 7: The impact of repeater failure on nodes under uniform probability of failure of repeaters. A node is unreachable
when all cables connecting to it fail. Error bars denote standard deviation computed over 10 trials.

Cables failed or
Nodes unreachable (%)

Submarine Cables
Submarine Nodes

55
50
45
40
35
30
25
20
15
10
5
0

S1 (High failure)

50 100 150

Alaska, on the other hand, loses all its long-distance connectivity
except its link to British Columbia in Canada.

Intertubes Cables
Intertubes Nodes

China: With low failures (( 2 ), about 56% of connections are una�ected. However, the densely populated city of Shanghai loses
all its long-distance connectivity even under this scenario. This isbecause all cables connecting to Shanghai are at least 28, 000 km
and interconnect multiple cities. Under high failures (( 1 ), Chinaloses all its long-distance cables except one (connecting to Japan,
Philippines, Singapore, and Malaysia).

S2 (Low failure)

India: The majority of cables connecting to India are una�ected,
and none of the cities are disconnected at low failure probabilities(( 2 ). Even under the high-failure scenario (( 1 ), some international
connectivity remains (e.g., India to Singapore, Middle East, etc.).
Unlike in China, the key cities of Mumbai and Chennai do not lose
connectivity even with high failures.

50 100 150

Inter-repeater distance (km)Singapore: Even under high failures, several cables connecting
to Singapore remain una�ected. Reachable destinations under ( 1include Chennai (India), Perth (Australia), Jakarta (Indonesia), etc.

Figure 8: Cable and nodes failures under two states of nonuniform repeater failures (( 1 , ( 2 ). In each state, repeaters in a
cable are assigned a failure probability based on the highest
latitude (!) endpoint of the cable. Three levels of failure are! > 60, 40 < ! < 60, and ! < 40. Assigned failure probability
per repeater in ( 1 is [1, 0.1, 0.01] and in ( 2 is [0.1, 0.01, 0.001]
across the three levels respectively.

UK: While the UK loses most of its long-distance cables under thehigh failure scenario, its connectivity to neighboring European locations such as France, Norway, etc. remains. However, connectivity
to North America is lost.

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connectivity to Europe under this failure scenario, but Brazil doesnot. This is because the Ellalink cable connecting Brazil to Portugal
is 6, 200 km, while the cable connecting Florida to Portugal is much
longer at 9, 833 km.
Across several countries, there are short cables interconnecting
nearby nodes. For example, cables interconnecting various islands
in Hawaii or that connecting two landing stations in Rhode Island.
Across both high- and low-latitude locations on all continents, suchcables are una�ected even under high repeater failure rates.

ASes with presence
above threshold (%)

100
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60
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90

80

70

60

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|Latitude| threshold(a) Reach of ASes1

4.4.1 Implications for Autonomous System Connectivity.
Ideally, we want to understand the ASes that will be a�ected whensubmarine cables fail. However, this will require AS to cable mapping, which is currently unavailable. Fortunately, CAIDA provides
a dataset with Internet router locations and router to AS mappings.
This dataset contains router to AS mapping for 46, 014, 869 routers
across 61, 448 ASes. We use this dataset to understand the impact
on ASes. Note that there exist several known errors in the location mapping employed by the CAIDA dataset [34]. Hence, theevaluations in this paper are a�ected by these errors as well.
First, the impact on an AS depends on its presence in the vulnerable latitude region (above 40> ). Hence, we measure the percentage
of ASes that have at least one router instance in this region. In
Figure 9(a), we observe that 57% of ASes have a presence in thevulnerable regions.
Second, an AS has a higher probability of being a�ected if it has
a large spread. We measure the spread of an AS using the location
coordinates of its routers. The evaluation is restricted to latitude
spread since the estimation of longitude boundaries is more di�cultand error-prone with the given dataset. An AS’ latitude spread ismeasured as the di�erence between the highest and the lowest
latitudes of its component routers. In Figure 9(b), we observe that
50% of ASes have a latitude spread less than 1.723> and 90% of ASes
have a latitude spread less than 18.263> (1> latitude spread ⇡ 111km). This shows that a vast majority of ASes are geographically
localized and do not have a large spread.
A geographically restricted AS is less likely to be directly impacted by solar superstorms. A large spread for an AS may notalways mean that it has long links. However, with a large spread, it
is likely that an AS will be directly impacted or indirectly impacted
within one hop by a severe event.
Overall, a large number of ASes with low spread may help in
limiting the impact. However, a signi�cant fraction of ASes havesome presence in the more vulnerable regions.

CDF

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0.6
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40

60

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120

Systems Resilience

In the previous section, we analyzed the impact on the physicalinfrastructure. The impact on Internet users will, however, dependon systems that run on this physical infrastructure. In this section, we discuss our investigation on Autonomous Systems (ASes),
hyperscale data centers, and DNS servers.

140

Spread of ASes (degrees of latitude)(b) Spread of ASes

Figure 9: (a) An AS has a reach above a particular latitude
if it has at least one router with latitude coordinates above
the threshold. ASes above 40> are more vulnerable. (b) The
spread of an AS, measured using the location coordinates ofrouters in the AS, is the di�erence between the highest andthe lowest latitudes of its component routers. Note that 1>
latitude spread approximately equals to 111 km.
South Africa: Even under the high-failure scenario (( 1 ), although
it loses some capacity, South Africa continues to retain its connectivity to both the Eastern and the Western Coast of Africa. The
cable interconnecting South Africa and Portugal (and other intermediate locations including Nigeria), as well as South Africa andSomalia (and other intermediate locations including Mozambiqueand Madagascar), are una�ected.
Australia and New Zealand: With high failures (( 1 ), New Zealandloses all its long-distance connectivity except to Australia. Similarly,
while Australia retains most of its connectivity to nearby islands
in addition to domestic connections, the longest una�ected cableinterconnects it with Jakarta (Indonesia) and Singapore.

4.4.2 Implications for Hyperscale data centers. Based on
the characteristics of solar superstorms-based failures at the levelof nodes, cities, and countries in the previous section, next we discuss the implications for large-scale data centers that serve a large
fraction of Internet services and content today. In particular, weBrazil: Interestingly, even under high failures (( 1 ), Brazil will retain its connectivity to Europe in addition to other parts of SouthAmerica such as Argentina. However, it will lose its connectivity
to North America. It is interesting to note that the US loses its

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consider public information on data center locations of Google [6]and Facebook [38].
While the majority of Google data centers [6] are located in theUS, they are spread across latitudes and longitudes. In the event ofhigh failures, data centers in South Carolina and Georgia as well as
Las Vegas are more likely to be located close to active long-distancecables. In Asia, one of Google’s data centers is located in Singapore,a location less likely to lose connectivity during solar superstorms.
European Google data centers are located in countries with shortinterconnecting submarine cables to the rest of Europe. While thisarea is highly susceptible to power failures, Internet failures due
to repeater damage are less probable due to the shorter length ofcables in this region.
Facebook’s data centers [38] are predominantly located in thenorthern parts of the Northern hemisphere. Facebook does notoperate any hyperscale data centers in Africa or South America,
unlike Google. Owing to the limited geographic spread of data
centers, Facebook will have less resilience in the event of solar
superstorms.

• A large fraction of ASes have a presence in vulnerable regions,however, the vast majority have a small spread. The extent of
impact on an AS and its customers will depend on a combinationof these factors.
• Google data centers have a better spread, particularly in Asia andSouth America. Facebook is more vulnerable.
• DNS root servers are highly distributed and hence not vulnerable.

5

5.1

Internet Infrastructure Design

As shown in our analysis, the current Internet infrastructure isheavily concentrated in higher latitudes that are at a greater risk
for GIC. We need to factor in this threat during infrastructure
expansion.
With the increased melting of Arctic ice, there are ongoing e�orts
to lay cables through the Arctic [11, 12]. While this is helpful for
improving latency, these cables are prone to higher risk. During
topology design, we need to increase capacity in lower latitudes
for improved resiliency during solar storms (although latency ishigher). Moreover, since links from the US and Canada to Europe
and Asia are highly vulnerable, adding more links to Central andSouth America can help in maintaining global connectivity. SouthAmerica is more likely to maintain connectivity to Europe andAfrica.
At submarine cable landing points, particularly in the low latitudes, it is important to have mechanisms for electrically isolatingcables connecting to higher latitudes from the rest, to prevent cascading failures.
A higher density of data centers and IXPs in northern parts ofEurope and America also poses a threat to Internet services. Data
center and application service providers should be cognizant ofsolar threats during new deployments. We need to develop standardized tests for measuring end-to-end resiliency of applications
under such extreme events. Speci�cally, systematic modeling ofpotential disruptions to the Internet, from the physical layer tovarious applications, through collaborations between astrophysicists, electrical engineers, and networking researchers is critical for
improving Internet resiliency.
This paper focused on terrestrial infrastructure only. With increasing deployments of low earth orbit satellites [14], it is also
important to study the impact of solar superstorms on satellite
Internet constellations that are directly exposed to powerful CMEs.

4.4.3 Implications for DNS servers. DNS root servers arehighly geographically distributed. Although the distribution is notproportional to Internet users (Africa with more Internet users thanNorth America has nearly half the number of DNS servers), DNS
root servers are widely present on all continents. Hence, DNS root
servers are resilient in the face of solar superstorms.
While location information of all DNS root servers is publicly
available (including all anycast server locations), data on more than
1500 Top-Level Domain servers and other DNS root zone servers arelimited. While the IP address of these servers can be obtained from
DNS records, it is di�cult to identify their locations, particularly
because they typically employ anycast.
4.4.4

PLANNING FOR THE FUTURE

Although we have sentinel spacecraft that can issue early warnings of CMEs providing at least 13 hours of lead time, our defenses
against GIC are limited. Hence, we need to prepare the infrastructure for an eventual catastrophe to facilitate e�cient disastermanagement. Towards this goal, we outline several directions.

Summary.

• The distribution of Internet infrastructure is skewed when compared to the distribution of Internet users. The infrastructure
concentration in higher latitudes poses a greater risk duringsolar storms.
• The investigation on the impact of GIC shows that submarine
cables have a higher risk of failure compared to land cables. Thisis primarily due to their large lengths.
• The US is one of the most vulnerable locations with a high riskof disconnection from Europe during extreme solar events. Intracontinental connections in Europe are at a lower risk due to thepresence of a large number of shorter land and submarine cables
interconnecting the continent.
• In Asia, Singapore will retain good connectivity to neighboring
countries even under severe storms. Chinese cities are more prone
to lose connectivity than Indian cities because they connect tomuch longer cables. The cables running along the eastern andwestern coasts of Africa are less prone to failures.
• The cable between Brazil and Europe has less probability of beinga�ected compared to cables connecting the US and Europe.
• Australia, New Zealand, and other island countries in the region
will lose most of their long-distance connections. But local connectivity, as well as connections to Singapore, are less vulnerable.

5.2

How to Use the Lead Time?

A CME that originates in the sun reaches the earth at least 13 hours,
typically 1-3 days, later. This provides the infrastructure operators
an opportunity to devise a shutdown strategy that minimizes connectivity loss during and after impact. In power grids, since GIC
is superimposed with generated current, a key strategy involves
reducing or completely shutting down power generation.

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In the Internet infrastructure, the shutdown strategy needs tofocus on two aspects: (i) protecting the equipment during a solar
event, and (ii) ensuring the continuation of services after the event
anticipating partial damage to infrastructure (damaged submarine
cables, satellites, etc.). Similar to power grids, powering o� is the easiest solution for equipment damage prevention. However, note that
this only provides limited protection since GIC can �ow througha powered-o� cable. Since the peak current is reduced slightly bypowering o�, this can help only when the threat is moderate.
Planning for post-impact connectivity is a much harder problem, especially with limited modeling available for the extent ofcable damage. Search engines, �nancial services, etc. should geodistribute critical data and functionalities so that each partition(potentially disconnected landmasses such as N. America, Eurasia,
Australia, etc.) can function independently. Also, service and content providers should pre-provision high priority service for criticalapplications such as 911, hospitals, �re departments, etc.

5.374]. Today, there is a tight interdependence between power grids
and networks. Smart grids rely on either their own private WAN
networks or the public Internet for their functioning. As a result,
failures of power grids and the Internet and other communication
networks are more tightly coupled.
Although power grids and the Internet have varying levels of
impact on society, there are several di�erentiating features in thenature of failures and hence, the recovery mechanisms required.
First, power grids and the Internet di�er in their structure andorganization. For example, in the US, there are three regional power
grids. If the power grid in New York fails, it will not cause any
signi�cant e�ects in California. The Northeast will be without
power, but it is not possible to transfer electricity from California
to New York and cause any power overload in California. On theother hand, when all submarine cables connecting to NY fail, there
will be signi�cant shifts in BGP paths and potential overload inInternet cables in California. In short, the Internet is more global
compared to power grids, and even regional failures can result insigni�cant consequences for the broader Internet.
Second, the length of power cables will closely follow the ITU
land dataset. Both power lines and Internet cables on land are typically in close proximity to road and rail networks. Hence, longer
submarine cables may be susceptible to higher risks. The key roadblock associated with replacing transformers in power grids is the
manufacturing time for new equipment. For submarine cables, inaddition to equipment availability, access to failure location forrepair is also a challenge.
Modeling of the interdependence of power grids and the Internet,
potential cascading failures between the two systems due to theircoupling, and design of recovery mechanisms for the mutually dependent systems are problems that need to be tackled for increasing
the robustness of our critical infrastructure.

Piecing Together a Partitioned Internet

We need to rethink the network environment in the event of apartial or complete disconnection [57]. This includes designing adhoc network connectivity mechanisms (e.g., Project Loon [36]) andpeer-to-peer applications that can bootstrap connectivity locally.
User-powered mesh networks [7] that proved valuable during othernatural disasters such as earthquakes can also help during solar
superstorms. However, unlike other localized disasters, wide-areaconnectivity disruption is a unique challenge associated with solar
storms. To tackle this problem, we need to examine alternatives
such as backup interdomain protocols that allow multiple paths
and more resilient Internet architectures (e.g., SCION [18]). More
broadly, designing and installing in advance a seamless protocol thatcan piece together all available modes of communication, including cables, satellites, and wireless, across multiple administrative
domains is critical for fast recovery.

5.4

6Devising New Resilience Tests for Internet
Systems

Current best practices on fault tolerance and resilience evaluation
in software systems revolve around failure models that consider alimited number of failures within and across locations. Large-scale
infrastructure failures spanning broad swaths of the Internet areabsent in the literature. This was primarily due to the fortuitous
absence of such a catastrophic event in the past two decades whenthe Internet infrastructure grew rapidly. Hence, our understandingof the impact of a solar superstorm on Internet sub-systems (e.g.,
Autonomous Systems) and Internet-based systems (e.g., cloud services, Voice over IP) is very limited. We need to devise standardpractices in resilience testing involving large-scale failures.

5.5

CONCLUSION

In this paper, we show that a powerful solar superstorm has the potential to cause massive disruption of the Internet. Astrophysicists
estimate the likelihood of a solar storm of su�cient strength tocause catastrophic disruption occurring within the next decade tobe 1.6 12%. Paying attention to this threat and planning defenses
against it, like our preliminary e�ort in this paper, is critical forthe long-term resilience of the Internet. Several challenges remain
open in this space. How can we model infrastructure failures moreaccurately? How do we factor in solar threat during Internet infrastructure and systems design? How can we help operators inmaking disaster preparation and recovery plans? We anticipate that
this paper will provide an initial impetus towards answering theseimportant questions.

ACKNOWLEDGMENTS

Interdependence with Power Grids

I sincerely thank the anonymous reviewers, the shadow shepherd,
and the shepherd, Adrian Perrig, for their helpful comments andfeedback. I would also like to thank Kishor Kumar Kalathiparambil
for verifying the correctness of astrophysics discussions in thispaper, and Aditya Akella and Sujata Banerjee for their feedback on
an early version of the paper.

The economic impact of Internet disruption for a day in the US isestimated to be $7 billion [1], while the same due to electric grid
failure is estimated to be more than $40 billion [60]. The power gridis the most critical infrastructure which almost all other systems
rely on. The impact of solar superstorms on power grids has beenan active area of research over several decades [20, 40, 41, 43, 55,

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