1. Implementa)on
Step1:
• Master
performs
handshake
with
all
the
nearby
peers
(iDevices)
which
offer
the
service
of
MapReduce
using
MulAPeer
ConnecAvity.
• Job
is
to
count
the
occurrences
of
alphabet
(A,
E,
W,
Z)
in
a
data
file.
• The
data
file
is
a
dicAonary
of
words
with
more
than
5
million
characters.
• Avg
Speedup:
2.56
Introduc)on
• A
single
iDevice
like
iPhone
or
iPad
does
not
have
enough
processing
power
to
process
computaAonally
complex
funcAons
on
large
data
sets.
• This
project
envisions
a
mesh
of
inter-­‐connected
iDevices
to
accomplish
a
task
like
that.
• How?
By
working
together
on
a
single
data
set.
Background
Conclusions
• It
can
be
noAced
that
the
Ame
required
to
perform
the
job
is
inversely
proporAonal
to
the
number
of
peers
used.
• Using
this
methodology
many
client-­‐server
systems
can
be
transformed
into
clustered
client
systems,
such
as
face-­‐recogniAon.
Implemen'ng
MapReduce
framework
on
iOS
devices
using
Mul'Peer
Connec'vity
Student:
Varun
Goyal
Advisor:
Dr.
Peizhao
Hu
Rochester
InsAtute
Of
Technology,
One
Lomb
Memorial
Drive,
Rochester,
NY
14623
-­‐
5603
0
10
20
30
40
50
60
1
Peer
2
Peers
3
Peers
Count
of
A
Count
of
E
Count
of
W
Count
of
Z
Results
• This
was
recently
made
possible
when
Apple
released
Mul'Peer
Connec'vity(MPC)
as
a
part
of
iOS
7
release.
• MPC
makes
it
possible
for
iDevices
to
discover
services
provided
by
other
iDevices
near
by.
• Uses
basic
infrastructure
like
WiFi
or
bluetooth.
• MapReduce
with
word
count
example:
1. Master
splits
the
input
data
into
chunks
and
sends
it
to
each
worker
node
2. Map:
counts
the
number
of
occurances
for
each
key
3. Shuffle:
data
is
shuffled
based
on
key
4. Reduce:
adds
up
the
count
for
each
key
Step
2:
• User
selects
the
job
to
be
performed
• Master
divides
the
data
according
to
number
of
peers.
• Master
sends
data,
job,
and
command
to
run
Map
funcAon
to
peers
in
a
JSON.
Step
3:
• Peers
perform
Map
funcAon
on
their
data
set
• Peers
return
the
map
funcAon’s
output
to
Master
to
be
shuffled.
Step
4:
• Depending
the
number
of
unique
keys
and
the
number
of
peers
shuffled
data
from
step
3
is
divided
in
different
parts
and
sent
back
to
the
peers
to
run
reduce
funcAon
on
it.
Step
5:
• Peers
perform
reduce
funcAon
on
their
data
set.
• Peers
return
the
reduced
data
back
to
master.
• Master
displays
the
final
result.
References
• MulA-­‐peer
framework
by
Apple:
hgps://developer.apple.com/library/
prerelease/ios/documentaAon/MulApeerConnecAvity/Reference/
MulApeerConnecAvityFramework/index.html
• MulAPeer
ConnecAvity,
Apple
Docs.
hgps://developer.apple.com/library/
prerelease/ios/documentaAon/MulApeerConnecAvity/Reference/
MulApeerConnecAvityFramework/index.html
• Jeffrey
Dean
and
Sanjay
Ghemawat.
2008.
MapReduce:
simplified
data
processing
on
large
clusters.
Commun.
ACM
51,
1
(January
2008),
107-­‐113.
DOI=10.1145/1327452.1327492
hgp://doi.acm.org/
10.1145/1327452.1327492
Contact
Info
Name:
Varun
Goyal
Email:
varungoyal44@gmail.com
Phone:
+1
585
743
8102
Future
Work
Technical
Challenges
• Cannot
inject
code
yet
hence
cannot
supply
user
defined
job.
• Need
individual
device’s
permission
to
join
the
network
every
Ame
a
peer
needs
to
join
the
system
which
provides
added
security
from
Apple
but
reduces
the
technical
enhancements
and
new
possibiliAes.
• Face
RecogniAon
job
was
implemented
using
this
system,
however
due
to
shortage
of
Ame
the
actual
task
of
performing
face-­‐recogniAon
is
performed
using
Kairos
API.
• Once
a
soluAon
is
found
to
inject
code
into
the
system,
which
Apple
can
approve,
the
framework
could
be
enhanced
to
perform
user
defined
jobs.
• Face-­‐RecogniAon
algorithm
could
be
implemented
on
iOS
and
provided
as
a
job
to
this
framework
to
determine
the
effecAveness
of
the
framework
and
viability
of
the
algorithm
to
perform
face
recogniAon
in
fixed
amount
of
Ame.
• If
Apple
updates
the
MulAPeer
ConnecAvity
framework
to
be
able
to
use
system
resources
in
the
background
(like
an
Ideal
Mode)
this
framework
can
be
used
with
minimum
user
interacAon.
0
20
40
60
80
100
120
140
160
180
200
1
Peer
2
Peers
3
Peers
Dear
Bear
Car
Tree
• Job
is
to
count
the
occurrences
of
words
(Dear,
Bear,
Car,
Tree)
in
a
data
file.
• The
data
file
is
a
dicAonary
of
words
with
more
than
83
million
characters.
• Avg
Speedup:
2.97