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savoirfairelinux
opendht
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a8cfdbe7
Unverified
Commit
a8cfdbe7
authored
8 years ago
by
Simon Désaulniers
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doc: add connectivity loss search analysis tex file
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doc/connectivity-loss-by-search-criteria.tex
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a8cfdbe7
\documentclass
[11pt]
{
article
}
\usepackage
[utf8x]
{
inputenc
}
\usepackage
[top=2cm,bottom=2cm]
{
geometry
}
\usepackage
{
forloop
}
\newcounter
{
counter
}
% maths
\usepackage
{
amssymb
}
\usepackage
{
amsmath
}
\usepackage
{
mathrsfs
}
\usepackage
{
shadethm
}
\usepackage
{
amsthm
}
\newshadetheorem
{
shadeDef
}{
Definition
}
[section]
\newtheorem
{
remark
}{
Remark
}
[section]
\renewcommand
{
\P
}{
\mathbb
{
P
}}
\usepackage
{
float
}
\usepackage
{
booktabs
}
\setlength
{
\parindent
}{
0ex
}
\setlength
{
\parskip
}{
0.5em
}
\begin{document}
\title
{
Annex 1: Probabilistic analysis of connectivity changes
}
\author
{
Adrien Béraud, Simon Désaulniers, Guillaume Roguez
}
\maketitle
\pagestyle
{
empty
}
\begin{shadeDef}
A node flagged as
\emph
{
``expired''
}
by a node
$
n
$
is a node which has not responded to any
of
$
n
$
's last three requests.
\end{shadeDef}
\begin{remark}
An expired node will not be contacted before 10 minutes from its expiration time.
\end{remark}
Let
$
N
$
the DHT network,
$
n
_
0
\in
N
$
, a given node and the following probabilistic events:
\begin{itemize}
\item
$
A
$
:
$
\forall
n
\in
N
$
$
n
$
is unreachable by
$
n
_
0
$
,
\emph
{
i.e.
}
$
n
_
0
$
lost connection
with
$
N
$
;
\item
$
B
$
:
$
S
\subset
N
$
, the nodes unreachable by
$
n
_
0
$
with
$
k
=
{
|S|
\over
|N|
}$
;
\item
$
C
$
:
$
m
\le
|N|
$
nodes are flagged as ``expired''.
\end{itemize}
We are interested in knowing
$
\P
(
A|C
)
$
,
\emph
{
i.e.
}
the probability of the event where
$
A
$
occurs
prior to
$
C
$
. From the above, we immediately get
$$
\left\{
\begin
{
array
}{
ll
}
\P
(
C|A
)
&
=
1
\\
\P
(
A
)
+
\P
(
B
)
&
=
1
\end
{
array
}
\right
.
$$
Also, the event
$
A|C
$
can be abstracted as the urn problem of draw without replacement. Then,
$$
\P
(
C|B
)
=
\prod
_{
i
=
0
}^
m
\left
[
k|N|
-
i
\over
|N|
\right
]
=
\prod
_{
i
=
0
}^
m
\left
[
k
-
{
i
\over
|N|
}
\right
]
$$
Furthermore, using Bayes' theroem we have
\begin{align*}
\P
(A|C)
&
=
{
\P
(C|A)
\P
(A)
\over
\P
(C|A)
\P
(A) +
\P
(C|B)
\P
(B)
}
\\
&
=
{
\P
(A)
\over
\P
(A) +
\P
(C|B)
\P
(B)
}
\\
&
=
{
\P
(A)
\over
\P
(A) +
\P
(C|B)
\left
[1 - \P(A)\right]
}
\\
\Rightarrow
\forloop
{
counter
}{
0
}{
\value
{
counter
}
< 5
}{
\qquad
}
\quad
\P
(A)
&
=
\P
(A|C)
\left
[\P(A) + \P(C|B)\left(1 - \P(A)\right) \right]
\\
\Rightarrow
\forloop
{
counter
}{
0
}{
\value
{
counter
}
< 2
}{
\qquad
}
\;\:\,
\P
(A)
\left
[{ 1 \over \P(A|C)} - 1\right]
&
=
\P
(C|B)
\left
(1 -
\P
(A)
\right
)
\\
\end{align*}
Finally,
\begin{equation}
\label
{
eq:final
}
\left
[{\P(A) \over 1 - \P(A)}\right]
\left
[{ 1 \over \P(A|C)} - 1\right]
=
\prod
_{
i=0
}^
m
\left
[k - { i \over |N| }\right]
\end{equation}
From
\eqref
{
eq:final
}
, we may set a plausible configuration
$
\{\P
(
A
)
,
\P
(
A|C
)
,k,|N|
\}
$
letting us
produce results such as in table
\ref
{
tbl:k
_
1
_
2
}
,
\ref
{
tbl:k
_
2
_
3
}
and
\ref
{
tbl:k
_
3
_
4
}
.
\begin{table}
[H]
\centering
\caption
{
The values for
$
m
$
assuming
$
\P
(
A|C
)
\ge
0
.
95
,
\,
k
=
{
1
\over
2
}$}
\label
{
tbl:k
_
1
_
2
}
\begin{tabular}
{
lcccc
}
\toprule
$
|N|
\diagdown
\P
(
A
)
$
&
${
1
\over
10
}$
&
${
1
\over
100
}$
&
${
1
\over
1000
}$
&
${
1
\over
10000
}$
\\
\midrule
$
2
^
0
$
&
1
&
1
&
1
&
1
\\
$
2
^
1
$
&
1
&
1
&
1
&
1
\\
$
2
^
2
$
&
2
&
2
&
2
&
2
\\
$
2
^
3
$
&
4
&
4
&
4
&
4
\\
$
2
^
4
$
&
5
&
6
&
7
&
8
\\
$
2
^
5
$
&
5
&
7
&
9
&
10
\\
$
2
^
6
$
&
6
&
9
&
11
&
13
\\
$
2
^
7
$
&
6
&
9
&
12
&
14
\\
$
2
^
8
$
&
7
&
10
&
13
&
16
\\
$
2
^
9
$
&
7
&
10
&
13
&
16
\\
$
2
^{
10
}$
&
7
&
10
&
13
&
17
\\
\bottomrule
\end{tabular}
\end{table}
\begin{table}
[H]
\centering
\caption
{
The values for
$
m
$
assuming
$
\P
(
A|C
)
\ge
0
.
95
,
\,
k
=
{
2
\over
3
}$}
\label
{
tbl:k
_
2
_
3
}
\begin{tabular}
{
lcccc
}
\toprule
$
|N|
\diagdown
\P
(
A
)
$
&
${
1
\over
10
}$
&
${
1
\over
100
}$
&
${
1
\over
1000
}$
&
${
1
\over
10000
}$
\\
\midrule
$
2
^
0
$
&
1
&
1
&
1
&
1
\\
$
2
^
1
$
&
2
&
2
&
2
&
2
\\
$
2
^
2
$
&
3
&
3
&
4
&
4
\\
$
2
^
3
$
&
5
&
5
&
6
&
8
\\
$
2
^
4
$
&
6
&
8
&
9
&
10
\\
$
2
^
5
$
&
8
&
10
&
12
&
14
\\
$
2
^
6
$
&
9
&
13
&
16
&
18
\\
$
2
^
7
$
&
11
&
15
&
18
&
22
\\
$
2
^
8
$
&
11
&
16
&
21
&
25
\\
$
2
^
9
$
&
12
&
17
&
22
&
27
\\
$
2
^{
10
}$
&
12
&
18
&
23
&
28
\\
\bottomrule
\end{tabular}
\end{table}
\begin{table}
[H]
\centering
\caption
{
The values for
$
m
$
assuming
$
\P
(
A|C
)
\ge
0
.
95
,
\,
k
=
{
3
\over
4
}$}
\label
{
tbl:k
_
3
_
4
}
\begin{tabular}
{
lcccc
}
\toprule
$
|N|
\diagdown
\P
(
A
)
$
&
${
1
\over
10
}$
&
${
1
\over
100
}$
&
${
1
\over
1000
}$
&
${
1
\over
10000
}$
\\
\midrule
$
2
^
0
$
&
1
&
1
&
1
&
1
\\
$
2
^
1
$
&
2
&
2
&
2
&
2
\\
$
2
^
2
$
&
3
&
3
&
3
&
3
\\
$
2
^
3
$
&
5
&
6
&
6
&
6
\\
$
2
^
4
$
&
7
&
9
&
10
&
11
\\
$
2
^
5
$
&
10
&
12
&
14
&
16
\\
$
2
^
6
$
&
12
&
16
&
19
&
22
\\
$
2
^
7
$
&
14
&
19
&
23
&
27
\\
$
2
^
8
$
&
15
&
21
&
27
&
32
\\
$
2
^
9
$
&
16
&
23
&
30
&
36
\\
$
2
^{
10
}$
&
17
&
24
&
31
&
38
\\
\bottomrule
\end{tabular}
\end{table}
\end{document}
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