tervals, this may become problematic. Since most decentralized solutions would require the user to visit
the content (for example, be on the
website), the real question is, what is
the average page-view time for users
across the globe?
Turns out it is 3 minutes 36 seconds, 24 or 216,000 milliseconds.
To double-check this, I took all
peer-session times (the amount of
time Edgemesh peers were online and
connected to the mesh) across the
Edgemesh user base for the past six
months (Figure 4). The average was
right in line at 3 minutes 47 seconds.
In either case, if the node stays
online just long enough to download a single webpage, that would be
enough time for the data to circumnavigate the globe 270 times, certainly long enough to contact a peer
anywhere on Earth.
Capacity. If enough users are online
for a long enough duration, and they
have an acceptable egress throughput
(upload bandwidth), all that remains is
the question of whether there is enough
spare capacity (disk space) available to
provide a functional network.
If we assume a site has 20% of its
users on mobile and 80% of its users
on desktops—and further expand this
to 500MB of available capacity per
desktop user and 50MB per mobile
user (the lower end of browser-avail-able storage pools)—we can extract
an estimated required mesh size to
achieve a given cache hit rate if the
requested content follows a Zipf distribution. 1 Essentially, a website with
500GB of static content, that is, about
16 million average Web images, would
need an online capacity of two million
distinct nodes to achieve a theoretical
offload of 100% of its traffic to a P2P
mesh (approximately an 8: 1 ratio of
images to users).
Enabling a Distributed Internet
Now that we have better defined the
problems and established the
theoretical feasibility of a new solution, it’s
time to look at the technology available to bring to bear on the problem.
To start, we can constrain our focus a
bit. Implementations such as IPFS focus on distributing the entire content
base, allowing you to free yourself from
the restrictions of Web servers and
Figure 3. Mary Meeker’s 2017 smartphone analytics.
2009
3500
3000
2500
2000
1500
1000
500
0
70
60
50
40
30
20
10
0
2010
G
l
ob
a
lSm
a
rt
ph
o
ne
I
n
sta
l
le
dBa
s
e(
M
M)
2011 2012
global smartphone installed base (MM) Y/Y growth (%)
Y
/y
G
ro
wt
h(
%)
2013
Global Smartphone Installed Base = 2.8b...
+12% Y/y Vs. +25% (2015) / +37% (2014)
2014 2015 2016
2009
1500
1200
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0
50
40
30
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2010 G l o b
a
lSm
a
r
tp
ho
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U
nit
Shi
pme
nt
s(
M
M)
2011 2012
other
iOS
Android
Y/Y growth (%)
Y/y
G
r
ow
th
(%
)
2013
Global Smartphone Unit Shipments = Continue To Slow...
+3% Y/y Vs. +10% (2015) / +28% (2014)
2014 2015 2016
Figure 4. Histogram of peer duration.
00:00:00 00:00: 20 00:00: 40 00:01:00 00:01: 20 00:01: 40 00:02:00 00:02: 20 00:02: 40 00:03:00 00:03: 20 00:03: 40 00:04:00 00:04: 20 00:04: 40 00:05:00 :06:00 :05: 20 00:05: 40
7
6
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2
1
0
90
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40
30
20
10
0
Pe
rce
nt
of
Tot
al
Se
s
si
on
s
Session Duration
Distribution of Peer Connected Durations
( 30 Second Buckets)
C
um
ul
ati
ve
Per
ce
nt
of
S
amp
le
s
