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CCNxCon2012: Session 5: A Distributed PIT Table

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A Distributed PIT Table …

A Distributed PIT Table
Wei You, Bertrand Mathieu, Gwendal Simon (France Telecom Orange Labs)


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  • 1. A Bloom Filter basedDistributed PIT system 2nd  Workshop  CCNx  2012  in  Sophia  Antipolis   Wei  YOU,  Bertrand  MATHIEU,  Patrick  TRUONG,     Jean-­François  PELTIER,  Gwendal  SIMON   contact:  wei.you@orange.com      
  • 2. Objectives   Context  of  current  solutions:   PIT  executes  exact-­match    =>    huge  memory  space   Almost  every  incoming  packet  will  lead  a  change  at  the  PIT  entry  =>  fast   processing  task   With  conventional  solution  (e.g.  HashTable)  &  current  technologies  (SRAM,   RDRAM,  etc.)  =>  tradeoff  between  speed  and  memory  space     Objectives  of  our  study:    to  reduce  the  PIT  table  space  requirement  and   speed  up  the  lookup/update  process     Bloom  filter  is  a  possible  solution  since  it  is   fast  &  space-­efficient   Well  implemented  in  IP  applications   BUT  does  not  support  the  information  retrieval  =>  Thus  the  distributed   architecture  is  here.    =>  Our  Solution:  A  Bloom  Filter  based  Distributed  PIT  system  2   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 3. DiPIT:  a  distributed  Bloom  filter  based  PIT   Distributed  structure:  PITi   one  small  PITi  per  CCN  face   All  the  PITis  are  independent  from  each  other   each  PITi  is  a  counting  Bloom  filter   small  size,  fast  performance.     Reduction  of  false  positives  via  a  Shared  Bloom  Filter   Possible  mismatch  but  its  ratio  can  be  controlled  at  a  low  level   One  additional  binary  Bloom  filter,  shared  by  all  the  faces      3   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 4. PIT  table  size  estimation   Analyze  the  required  table  size  on  function  of  Interest  arrival  rate  ( in)   16  interfaces,   in  =  [0  ~  200Mpck/s],  RTT  =  80ms   fp  =  1%  and  0.1%,  SBF    =  1Mbits.   H-­bit  =  28  bits,  CCN  face  identifier  =  2Bytes    4   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 5. PIT  table  size  estimation   Analyze  the  required  memory  space  based  on  the  ratio  of  similar   interests  (same  content  name)   hash  table  is  better  only  when  80%  of  traffic  for  the  same  Interest  name  5   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 6. Implementations  in  CCNx  (release  0.4.2)   Implementation  in  CCNx  (0.4.2)   Initialization  of  the  face-­>cbf  is  according  to  the  face->flags The  ccnd_handle has  a  shared  binary  Bloom  filter  ccnd_handle-­>sbf     Le  ccnd_handle-­>sbf  has  a  RST  mechanism  which  is  triggered  by  the  number   of  inserted  elements     In  the  process  of  incoming  Interest  &  incoming  Content   Packets  are  filtered  with  the  face-­>flags     Lookup  &  update  the  Interest/Data  in  the  counting  Bloom  Filters  and  the  SBF   Binary  Bloom  filter    state  check  after  a  SBF  update   Get  the  match  results.      6   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 7. 1st  Evaluation:  in-­line  network   Real  testbed  in  Telecom  Bretagne   composed  of  9  CCNx  nodes   Client     Server   Settings   10000  ContentNames  (Interest  &  Data),  zipf  distribution,   =0.7   1  content  provider  and  1  clients.   9  nodes  in  line,  1Mbits  for  each  PITi,1Mbits  for  SBF,  2.5%  de  threshold  of   RST  in  SBF   Results:     The  client  (node  0)  generates  10000  Interests  on  4827  different  names       The  server  (node  8)  sends  4826  contents     DiPIT   Thus  the  false  positive  rate  in  PITi  is  1.7%   DiPIT  blocks  6  Interests  =>  The  packet  lost  rate  is  0.1%   2  times  RST  in  each  nodes.  7   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 8. 2nd    Evaluation:  Geant  network   Settings   Result   Geant,  1Mbit  PITi,  1Mbits  SBF   Node  0  sends  4372  +  16  +  395  =   2.5%  de  threshold  of  RST  in  SBF     4783  Interests.  Thus  there  are  4783    4761  =  18  Interests  which  get  lost   during  the  forwarding  process.  Total   PLR  in  the  path  =>  0.37%   395   Node  8  gets  4165  +  15  +  593  =   0   16   1   2   4765  Interests,  sends  4761   Contents.  Thus  the  PLR  in  node  8  4372   =>  0.08%   25   605     Node   RST  (times)   3   4   5     0   3   1   4   593   2   4  4165   3   5   101   15   6   7   8   4761  Data   4   5   5   5   6   4   4157   7   3   8   2   8   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 9. Where  to  deploy  such  a  solution:   Case  study:  a  hierarchical  network  topology   Topology   3  levels,  edge,  core  and  peering  routers.   Each  terminal:  10Mpck/s,   _interest  =  95%   Internal  link  delay  d  =  20ms.   Peering  link  delay  D  =  20ms       Recommendations  (e.g.  the  edge  router)   If  acceptable  fp  >0.01%  DiPIT  is  always  better  than  hash  tables   if  the    <  66Mpck/s,  it  is  better  to  use  RLDRAM  because  it  is   cheaper   If  the  acceptable  fp  <  0.01%,  the  hash  table  is  a  better  solution   However  when    >  86  Mpck/s  the  hash  table  can  no  more  be   used.  DiPIT  with  SRAM  is  the  only  option  9   A  Bloom  Filter  based  distributed  PIT  system   Wei  You   Wei  You  
  • 10. Conclusion   The  Bloom  Filter  based  distributed  PIT  architecture  (DiPIT)  can   significantly  reduce  the  memory  space  requirement  of   implementing  the  CCN  PIT  table,  with  a  small  acceptable  false   positive  ratio.   DiPIT  can  reduce  the  influence  of  the  current  memory   technology  bottleneck,  even  it  has  false  positive   Hash  table  has  the  limitations  at  the  table  size  and  the   performance  speed,  but  no  extra  network  load      10   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 11. Questions?    11  
  • 12. Backup Slides
  • 13. Hardware  challenge  for  the  hash-­based  PIT   Memory  chip    Trade-­off:  Processing  speed  OR  Storage  capacity     Technology   Access  time(ns)   Cost  ($/MB)   Max.  size   SRAM  (on-­chip)   1   27    50Mb     SRAM  (off-­chip)   4   27    250Mb     RLDRAM   15   0.27   2Gb   DRAM   55   0.016   10GB     Table  size  and  cost  vs.  Interest  arriving  rate   4  interfaces,   in  =  [0  ~  200Mpck/s],  RTT  =  80ms   Content  name  length  =  128bits   H-­bit  =  24/32/64  bits,  interface  identifier  =  2Bytes                                                            SRAM  (fast  for  processing)                                                                                                            RLDRAM(large  size  for  memory)  13   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 14. DiPIT:  a  Distributed  Bloom-­filter  based  PIT  table   Bloom  Filter   For  testing  the  existence  of  the  elements   Insert  -­-­  use  k  independent  hash  functions  to  insert  all  elements   in  an  empty  vector,  set  all  the  hash  result  positions  to  1   Testing    if  an  element  passed  through  all  the  hash  functions   could  have  a  result  all  1,  we  can  say  that  this  element  is  in  the   set   can  have  with  counters  for  deleting   Advantage  :  space  efficient   Drawback:  false  positive     How  to  retrieve  the  information?  14   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 15. DiPIT:  a  Distributed  Bloom-­filter  based  PIT  table   Algorithm     Wei  You  15   A  Bloom  Filter  based  distributed  PIT  system  
  • 16. Evaluation  results   Analyze  the  required  table  size  on  function  of  false  positive   probability   Only  when  k=3  and  fp  <  0.00003%,  hash  table  uses  less   memory  space  16   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  
  • 17. Evaluation  results   Analyze  of  the  traffic  burst   traffic  follows  the  Poisson  distribution   DiPIT  and  hash  table  are  both  designed  to  handle  100  Mpck/s   Interest   the  PLR  of  hash  table  increases  faster  after  100  Mpck/s  than   the  false  positive  of  DiPIT  17   A  Bloom  Filter  based  distributed  PIT  system   Wei  You  

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