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