decentralized k-means clustering with emergent computingdddas/afosr/resources/papers/... · 2014....
TRANSCRIPT
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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
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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
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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
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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
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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.
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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
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Clustering Overview • Grouping together similar data objects
• No correct answer – Unsupervised
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• Cluster centroid – Geometric center of cluster
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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
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QUESTIONS? 18