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Network Topologies - Barabasi & Power Laws

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A look at the ins and outs of power laws in networks, with some examples of when they can backfire.

A look at the ins and outs of power laws in networks, with some examples of when they can backfire.

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  • 1. Network  Topology   “more  than  just  a  pre/y  face”   Science  of  Visualiza8on                                                                   Avoiding  Cascading  Failures   Border  Gateway  Patrol               Conceptual  Controversy                                                      Nega8ve  Externali8es               Parasi8c  Compu8ng     Barabási’s  Model   Who’s  In  Control?     Digital  Switches:  Net  Terrorism   Subgroups  and  Neighborhoods                                Unintended  Consequences                   Self-­‐organizing  Networks     Epidemics  Reconsidered   Evolu8on  of  BGP:  Op8miza8on   Emergence                                                   A  Ques8on  of  Agency  
  • 2. Scien8fic  Visualiza8on  &  Sta8s8cs   Lorraine  Daston  &  Peter  Galison   Burch/Cheswick  Map  of  the  Web  
  • 3. Network  Protocol  Design      SSFNet,  a  visualiza8on  tool  for  internet  protocols  
  • 4. Border  Gateway  Patrol  (BGP)   AS   AS   Physical  Network  Topology   BGP  Rou8ng  Topology  
  • 5. Conceptual  Controversy   –  op8miza8on  vs.  random  preferen8al  aachment   From  “Luck  or  Reason”  –  Nature  2012;  Barabási  
  • 6. Barabási’s  Model    1/N  effect:  constant  system  size  increasing   Ƒd ∝da , a < 0 Faloutsos, et. al
  • 7. Who’s  in  control?   Small-­‐World  Systems   Ultra-­‐Large  Systems  
  • 8. Subgroups  and  Neighborhoods   from  Cornell  University’s  “Computa8onal  Methods  for  Nonlinear  Systems”  course  
  • 9. Complexity  of   managing                      3G/ 4G  networks     Mul8-­‐layer,  mul8-­‐ technology  networks     Quality  of  service   requirements   Opera6onal  Drivers   Increased  demand       for  data  services     Increased  diversity       of  services     Reduced  revenue       per  delivery  bit     Pressure  to             become  compe88ve     Technology  Drivers   Market  Drivers   Why  do  we  need  self-­‐organizing  networks?   Labor-­‐intensive   opera8ons     Processes  need   manual  interven8on   to  obtain  opera8onal   or  deployment   savings     Processes  that  are  too   fast,  too  granular,  or   too  complex  for   manual  interven8on   Paraphrased  from  Celcite’s  Cops-­‐  SON  product  page  at   hp://www.celcite.com/cops/products/cops-­‐son/index.html      
  • 10. Evolu8on  of  BGP:  Op8miza8on   CAIDA  1999   BGP  Rou8ng  Table,  exhibi8ng   preferen8al  hub  behavior   BBC’s  adver8sed  BGP  rou8ng   table  before  the  2012   Olympics,  exhibi8ng   preferen8al  peering  behavior        
  • 11. Avoiding  Cascading  Failures   Alex  Abella’s  2009   Book  Cover   Link  to  Paul  Baran’s  2001   interview  with  Wired  
  • 12. Nega8ve  Externali8es   …maar  ‘n  grapje   Link  to  Jakob  Nielsen’s    1998  take  on     “Figh8ng  Linkrot”  
  • 13. Parasi8c  Compu8ng   Link  to  Inquirer  ar8cle  on     Anonymous-­‐credited  HSBC  aack   Link  to  Forbes  ar8cles  on     LulzSec  aack  on  CIA.gov   Metaphoric  DDoS  aack  
  • 14. Digital  Switches:  Net  Terrorism    
  • 15. Unintended  Consequences   Heavy-­‐tailed  TCP  session  model  from  SSFNet  
  • 16. Epidemics  Reconsidered   Link  to  INSEAD’s  interpreta8on  of  Taleb’s  Four  Quadrants,  The  Black  Swan  
  • 17. Emergence  
  • 18. A  Ques8on  of  Agency   Link  to  the  en8re  book    
  • 19. Wrap  Up   Link  to  Cody  Dunne’s  paper  on  improving  network  visualiza8on  readability  
  • 20. Extra  Works  Consulted   Afanasyev,  Alexander  et.  al.  “BGP  Rou8ng  Table:  Trends  and  Challenges”.  Laboratory  for  Advanced  Systems   Reseach.  (2010)  UCLA.     Balke,  Wolf-­‐Tilo  and  Wolf  Siberski.  “Random  Graphs,  Small-­‐Worlds,  and  Scale-­‐Free  Networks”  L3S  Research   Center.  (2007)  University  of  Hanover.     Barabasi,  Albert-­‐Laszlo.  “Luck  or  Reason”.  Nature.  (2012)  BarabasiLab.com.  MacMillan  Publishers.     Bu,  Tian  and  Don  Townsley.  “On  Dis8nguishing  between  Internet  Power  Law  Topology  Generators”.   Proceedings  IEEE  INFOCOM  2002,  The  21st  Annual  Joint  Conference  of  the  IEEE  Computer  and   Communica8ons  Socie8es.  (2002)  New  York,  USA.     Claffy,  KC.  “Internet  measurement  and  data  analysis:  topology,  workload,  performance  and  rou8ng  sta8s8cs”.   (1999)  Coopera8ve  Associate  for  Internet  Data  Analysis  [CAIDA.org]   D’Souza,  Raissa  M.  et.  al.  “Emergence  of  tempered  preferen8al  aachment  from  op8miza8on.  PNAS  104,  no.   15.  (2007):  6112-­‐6117.     Dunne,  Cody  and  Ben  Shneiderman.  “Mo8f  Simplifica8on:  Improving  Network  Visualiza8on  Readability  with   Fan  and  Parallel  Glyphs”.  HCIL  Tech  Report  (2012):  1-­‐11.  University  of  Maryland.     Jovanović,  Mihajlo.  “Modeling  Peer-­‐to-­‐Peer  Network  Topologies  Through  “Small-­‐World”  Models  and  Power   Laws”.  IX  CommunicaMons  Forum  Telfor.  (2001)  Belgrade,  Serbia.   Fabrikant,  Alex;  Elias  Koutsoupias;  and  Christos  H.  Papadimitrious.  “Heuris8cally  Op8mized  Trade-­‐offs:  A  New   Paradigm  for  Power  Laws  in  the  Internet”.  (2002)  Stanford.edu.     Loridas,  Panagio8s;  Diomidis  Spinellis,  and  Vasileios  Vlachos.  “Power  Laws  in  Sovware”.  ACM  TransacMons  on   SoNware  Engineering  and  Methodology  18,  no.1  (2008):1–26.  Athens  University  of  Economics  and   Business.     Mitzenmacher,  Michael.  “A  Brief  History  of  Genera8ve  Models  for  Power  Law  and  Lognormal  Distribu8ons”.   Internet  MathemaMcs  1,  no.  2  (2004):  226-­‐251   Shakkoai,  Srinivas  and  R.  Srikant.  “Network  Op8miza8on  and  Control”.  FoundaMons  and  Trends  in  Networking   2,  no.  3.  (2007):  271-­‐379.     Stumpf,  Michael  P.  H.  and  Mason  A.  Porter.  “Cri8cal  Truths  About  Power  Laws”.  Science  335  (2012):  665-­‐666.     “Ultra-­‐Large  Scale  Systems”.  Sovware    Engineering  Ins8tute.  (2009)  CarnegieMellon.