Decentralized K-Means Clustering with Emergent Computing

Transcription

1 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

2 Problem Big Data 80% of world s data from last 2 years Increased volume challenges data analysis Problems with centralized computa?on 1

3 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

4 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 3

5 Emergent Compu?ng Distributed Compu?ng Systems Swarm Intelligent Systems 4

6 Swarm Intelligent System Ar?ficial swarm inspired by biology Mul?- agent system opera?ng in an environment U?lize emergent behavior to solve problems 5

7 Swarm Example - Flocking Alignment Separa?on 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 Cohesion 6

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9 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 8

10 Ant Foraging - General 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 9

11 Ant Foraging - An Implementation [1] Ants deposit 2 pheromones Green lead to home, deposit while foraging Blue lead to food, deposit while returning home 1 ant hill Sta?onary 1 food unlimited Many ants [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,

12 [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,

13 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 Food Ant Hill Ant Path Not pictured: Ant 12

14 Ant Hill Moves 13

15 Clustering Overview Grouping together similar data objects No correct answer Unsupervised Cluster centroid Geometric center of cluster 14

16 Evaluation Agent- based simula?on in MASON for Java For each scenario: 100 runs, 10,000?me steps sensor layouts Random 4 squares of 4 sensors 15

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18 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

19 QUESTIONS? 18