Project 2 – Gossip Simulator

Project 2 – Gossip Simulator

• What to include:
– README file including group members, other requirements specified
below
– project2.tgz or project2.zip the code for the project
– project2-bonus.tgz/zip the code for the bonus part, if any
1 Problem definition
As described in class Gossip type algorithms can be used both for group communication and for aggregate computation. The goal of this project is to determine
the convergence of such algorithms through a simulator based on actors written
in F#. Since actors in F# are fully asynchronous, the particular type of Gossip
implemented is the so called Asynchronous Gossip.
Gossip Algorithm for information propagation The Gossip algorithm
involves the following:
• Starting: A participant(actor) it told/sent a roumor(fact) by the main
process
• Step: Each actor selects a random neighbor and tells it the rumor
• Termination: Each actor keeps track of rumors and how many times it
has heard the rumor. It stops transmitting once it has heard the rumor
10 times (10 is arbitrary, you can select other values).

Push-Sum algorithm for sum computation
• State: Each actor Ai maintains two quantities: s and w. Initially, s =
xi = i (that is actor number i has value i, play with other distribution if
you so desire) and w = 1
• Starting: Ask one of the actors to start from the main process.
• Receive: Messages sent and received are pairs of the form (s, w). Upon
receive, an actor should add received pair to its own corresponding values. Upon receive, each actor selects a random neighboor and sends it a
message.
• Send: When sending a message to another actor, half of s and w is kept
by the sending actor and half is placed in the message.
• Sum estimate: At any given moment of time, the sum estimate is s
w
where s and w are the current values of an actor.
• Termination: If an actors ratio s
w
did not change more than 10−10 in
3 consecutive rounds the actor terminates. WARNING: the values s
and w independently never converge, only the ratio does.
Topologies The actual network topology plays a critical role in the dissemination speed of Gossip protocols. As part of this project you have to experiment
with various topologies. The topology determines who is considered a neighboor
in the above algorithms.
• Full Network Every actor is a neighbor of all other actors. That is, every
actor can talk directly to any other actor.
•
neighbors.
• Line: Actors are arranged in a line. Each actor has only 2 neighbors (one
left and one right, unless you are the first or last actor).
•
2 Requirements
Input: The input provided (as command line to your project2) will be of the
form:
project2 numNodes topology algorithm
Where numNodes is the number of actors involved (for 2D based topologies
you can round up until you get a square), topology is one of full, 2D, line,
imp2D, algorithm is one of gossip, push-sum.
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3DGrid: Actors forma 2D grid. The actors can only talk to the grid
Imperfect 3DGrid: Grid arrangementbutone random otherneighbor
isselectedfrom the list of all actors(8+1neighbors).
Output: Print the amount of time it took to achieve convergence of the algorithm. Please measure the time using
... build topology
val b = System.currentTimeMillis;
..... start protocol
println(b-System.currentTimeMillis)
Actor modeling: In this project you have to use exclusively the actor facility
in F# (projects that do not use multiple actors or use any other form
of parallelism will receive no credit).
README file In the README file you have to include the following material:
• Team members
• What is working
• What is the largest network you managed to deal with for each type of
topology and algorithm
Report.pdf For each type of topology and algorithm, draw the dependency
of convergence time as a function of the size of the network. You can overlap
different topologies on the same graph, i.e. you can draw 4 curves, one for each
topology and produce only 2 graphs for the two algorithms. Write about any
interesting finding of your experiments in the report as well and mention the
team members.
You can produce Report.pdf in any way you like, for example using spreadsheet software. You might have to use logarithmic scales to have a meaningful
plot.
3 BONUS
In the above assignment, there is no failure at all. For a 30% bonus, implement node and failure models (a node dies, a connection dies temporarily or permanently). Write a Report-bonus.pdf to explain your findings (how
you tested, what experiments you performed, what you observed) and submit
project2-bonus.tgz/zip with your code. To get the bonus you must implement at least one failure model controlled by a parameter and draw plots that
involve the parameter. At least one interesting observation has to be made
based on these plots.
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