Decentralized K-Means Clustering with Emergent Computing

Ryan  McCune  &  Greg  Madey  

University  of  Notre  Dame,  Computer  Science  &  Engineering  Spring  Simula?on  Mul?-‐Conference  2014,  Tampa,  FL  

Student  Colloquium  Oral  Presenta?on  April  13,  2014  

Problem – Big Data

•  80%  of  world’s  data  from  last  2  years  

•  Increased  volume  challenges  data  analysis  

•  Problems  with  centralized  computa?on  

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Distributed Computing •  Connected  computers  – Nodes  and  edges  

•  Distributed  computa?on  – S?ll  central  coordinator  •  BoElenecks  – Not  Scalable  – Failure  prone  

•  Global  Informa?on  – Mgmt  Overhead  Limi?ng   2

Solution - Emergent Computation •  Global  behavior  emerges  from              interac?on  of  distributed  computers  –  Global  behavior  also  a  computa?on  

•  Decentralized  –  No  boElenecks  

•  Scalable  •  Robust  

–  Efficient  •  Each  parallel  computer  executes  simple  program  •  Complex  computa?on  emerges  

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Distributed  Compu?ng  Systems  

Swarm  Intelligent  Systems  

Emergent  Compu?ng  

Swarm Intelligent System

•  Ar?ficial  swarm  inspired  by  biology  

•  Mul?-‐agent  system  opera?ng  in  an  environment  

•  U?lize  emergent  behavior  to  solve  problems  

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Swarm Example - Flocking

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Separa?on  

Alignment  

Cohesion  

•  Move  with  speed  and  direc?on  •  Sight  radius  to  perceive  neighbors  

•  Adjust  movement  in  3  ways  based  on  neighbors  (leW)  

•  Coordinated  flock  emerges  – From  simple,  local  behaviors  

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Research •  Emergent  compu?ng  

–  Poten?al  to  solve  Big  Data  challenges  –  But  few  examples,  if  any  –  So  how?  

•  Look  at  swarms  that  do  computa?on  –  Then  figure  out  how  to  translate  to  distributed  systems  

•  Swarm  example-‐  “Ant  Foraging”  –  Well-‐known  –  Shortest-‐path  emerges  

•  Swarm  example-‐  “Decentralized  Clustering”  –  New,  based  off  “Ant  Foraging”  –  Clustering  emerges  

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Ant Foraging - General

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•  Ants  search  to  bring  food  back  to  nest  

•  Interac?on  with  environment  influences  future  ac?ons  – Deposit  pheromones  

•  Randomly  search  environment  – More  likely  to  follow  path  of  higher  pheromone  concentra?on  

•  Shortest  path  emerges  

Ant Foraging - An Implementation[1] •  Ants  deposit  2  pheromones  – Green  lead  to  home,  deposit  while  foraging  – Blue  lead  to  food,  deposit  while  returning  home  

 

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[1]  Panait,  Liviu,  and  Sean  Luke.  "A  pheromone-‐based  u?lity  model  for  collabora?ve  foraging."  Proceedings  of  the  Third  Interna?onal  Joint  Conference  on  Autonomous  Agents  and  Mul?agent  Systems-‐Volume  1.  IEEE  Computer  Society,  2004.  

•  1  ant  hill  –  Sta?onary  

•  1  food  –  unlimited  

•  Many  ants  

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[1]  Panait,  Liviu,  and  Sean  Luke.  "A  pheromone-‐based  u?lity  model  for  collabora?ve  foraging."  Proceedings  of  the  Third  Interna?onal  Joint  Conference  on  Autonomous  Agents  and  Mul?agent  Systems-‐Volume  1.  IEEE  Computer  Society,  2004.  

Decentralized Clustering •  Adapted  from  Ant  Foraging  – Many  food  instead  of  1  food  – Many  ant  hills  instead  of  1  ant  hill  

•  Ant  hills  can  move  (right)  – Only  1  pheromone  type,  not  2  

•  Deposit  when  looking  for  food  •  Follow  to  return  to  ant  hill  •  No  pheromone  leads  to  food  •  Once  any  food  is  found  randomly,        pheromone  leads  to  nearest  ant  hill  

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Food  

Ant  Hill  

Ant  Path  Not  pictured:  Ant  

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Ant  Hill  Moves  

Clustering Overview •  Grouping  together  similar  data  objects  

•  No  correct  answer  – Unsupervised  

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•  Cluster  centroid  – Geometric  center  of  cluster  

Evaluation •  Agent-‐based  simula?on  in  MASON  for  Java  •  For  each  scenario:  –  100  runs,  10,000  ?me  steps  –  16  –  4  –  100  

•  2  sensor  layouts  –  Random  –  4  squares  of  4  sensors   15

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Conclusions •  Explore  swarm  intelligent  computa?on  – How  to  translate  to  distributed  compu?ng  

•  Introduce  swarm  intelligent  clustering  –  Further  work  

•  Elaborate  behavior  •  Compare  centralized  clustering  

•  Applica?ons  of  swarms  –  Robust,  scalable,  adaptable,  computa?onally  efficient  

•  Further  explore  Emergence  17

QUESTIONS? 18