Simulation of Scale-Free Networks

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We present a new simulation tool for scale-free networks composed of a high number of nodes. The tool, based on discrete-event simulation, enables the definition of scale-free networks composed of heterogeneous nodes and complex application-level protocols. To satisfy the performance and scalability requirements, the simulator supports both sequential (i.e. monolithic) and parallel/distributed (i.e. PADS) approaches. Furthermore, appropriate mechanisms for the communication overhead-reduction are implemented. To demonstrate the efficiency of the tool, we experiment with gossip protocols on top of scale-free networks generated by our simulator. Results of the simulations demonstrate the feasibility of our approach. The proposed tool is able to generate and manage large scale-free networks composed of thousands of nodes interacting following real-world dissemination protocols.

Tecnologia

Gabriele D’Angelo <gda@cs.unibo.it> http://www.cs.unibo.it/gdangelo/ joint work with: Stefano Ferretti Department of Computer Science University of Bologna SIMUTOOLS 2009, Rome (Italy) Simulation of Scale-Free Networks

Presentation outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Scale-Free networks : definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Scale-Free networks : meaning and examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Simulation of scale-free networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Gossiping protocols : fixed probability ,[object Object],[object Object],[object Object],[object Object],Algorithm function INITIALIZATION() v ← CHOOSE_PROBABILITY() function GOSSIP(msg) for all n j in Π j do if RANDOM() < v then SEND(msg, n j ) end if end for

Gossiping protocols : fixed fanout ,[object Object],[object Object],Algorithm function INITIALIZATION() fanout ← RETRIEVE_FANOUT() function GOSSIP(msg) if fanout ≥ | Π j | then toSend ← Π j else SELECT_NODES() end if for all n j in toSend do SEND(msg, n j ) end for

Gossiping protocols : probabilistic broadcast ,[object Object],[object Object],Algorithm function INITIALIZATION() p b ← PROBABILITY_BROADCAST() function GOSSIP(msg) if (RANDOM() < p b or FIRST_TRANSMISSION()) then for all n j in Π j do SEND(msg, n j ) end for end if

PaScaS : the scale-free network simulator ,[object Object],[object Object],[object Object],[object Object],[object Object]

Experimental evaluation : model parameters Model parameters and simulation scenario Parameter Value number of nodes 3000, 6000, 9000, 12000 message generation exponential distribution mean = 50 time-steps cache size (local to each node) 10 slots message Time To Live (TTL) 6 (fixed prob. and fanout) 4 (conditional broadcast) probability of dissemination (v) 0.5 (i.e. 50%) fanout value 5 probability of broadcast ( p b ) 0.5 (i.e. 50%) simulated time 1000 time-steps (after building)

Experimental evaluation : execution architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experimental evaluation : sequential execution A single process is responsible to manage the whole simulation. The fixed probability has computational requirements higher than other gossip protocols

Experimental evaluation : parallel execution ,[object Object],[object Object],[object Object],[object Object],[object Object]

Experimental evaluation : parallel execution (fixed probability)

Experimental evaluation : parallel execution (fixed fanout)

Experimental evaluation : parallel execution (cond. broadcast)

Experimental evaluation : monolithic vs. parallel execution ,[object Object],[object Object],[object Object],green = parallel faster than monolithic Nodes Gossip #1 Gossip #2 Gossip #3 3000 3.46 -12.22 -9.1 6000 0.19 -4.49 -6.23 9000 -5.35 -0.63 -3.36 12000 -9.07 -0.25 -2.42

Experimental evaluation : adaptive parallel execution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experimental evaluation : adaptive parallel execution

Parallel execution vs. adaptive parallel execution ,[object Object],[object Object],[object Object],green = parallel faster than monolithic Nodes Gossip #1 Gossip #2 Gossip #3 3000 34.18 -1.33 19.75 6000 38.63 6.87 23.37 9000 30.97 11.07 24.59 12000 26.47 9.65 22.56

Conclusions and future work ,[object Object],[object Object],[object Object],[object Object]

For more information ,[object Object],[object Object],[object Object],[object Object]

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AI presentation and introduction - Retrieval Augmented Generation RAG 101

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WSO2CONMay2024OpenSourceConferenceDebrief.pptx

Jennifer Lim

Simulation of Scale-Free Networks

1. Gabriele D’Angelo <gda@cs.unibo.it> http://www.cs.unibo.it/gdangelo/ joint work with: Stefano Ferretti Department of Computer Science University of Bologna SIMUTOOLS 2009, Rome (Italy) Simulation of Scale-Free Networks

10. Experimental evaluation : model parameters Model parameters and simulation scenario Parameter Value number of nodes 3000, 6000, 9000, 12000 message generation exponential distribution mean = 50 time-steps cache size (local to each node) 10 slots message Time To Live (TTL) 6 (fixed prob. and fanout) 4 (conditional broadcast) probability of dissemination (v) 0.5 (i.e. 50%) fanout value 5 probability of broadcast ( p b ) 0.5 (i.e. 50%) simulated time 1000 time-steps (after building)

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12. Experimental evaluation : sequential execution A single process is responsible to manage the whole simulation. The fixed probability has computational requirements higher than other gossip protocols

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14. Experimental evaluation : parallel execution (fixed probability)

15. Experimental evaluation : parallel execution (fixed fanout)

16. Experimental evaluation : parallel execution (cond. broadcast)

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19. Experimental evaluation : adaptive parallel execution

20. Experimental evaluation : adaptive parallel execution

21. Experimental evaluation : adaptive parallel execution

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25. Gabriele D’Angelo <gda@cs.unibo.it> http://www.cs.unibo.it/gdangelo/ joint work with: Stefano Ferretti Department of Computer Science University of Bologna SIMUTOOLS 2009, Rome (Italy) Simulation of Scale-Free Networks

Simulation of Scale-Free Networks

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