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iof-tools / mrai_setter @ 67493fe0

Name Size Revision Age Author Comment
Readme.md 1.46 KB c9f0b2d1 over 1 year tiamilani stable state pre merge
milaniBGPLoad.py 4.86 KB c1397947 over 1 year tiamilani corrected DPC calculation, based on which node ...
mrai_setter.py 11.5 KB c9f0b2d1 over 1 year tiamilani stable state pre merge
test_mrai_setter.py 8.22 KB 62798f6e over 1 year tiamilani intermidiate state

Latest revisions

# Date Author Comment
85d6694b 01/12/2020 03:48 PM tiamilani

Merge branch 'master' into reorganizedVersion

c9f0b2d1 01/12/2020 03:45 PM tiamilani

stable state pre merge

9a18ba0c 01/11/2020 02:12 PM tiamilani

bug fixes

4f4fea5f 12/18/2019 09:38 AM tiamilani

mrai setter bounded

7b361a67 12/17/2019 04:00 PM tiamilani

updated mrai setter to correct fabrikant bug

7476b400 11/27/2019 08:49 AM tiamilani

Tutto pronto per test su fig3 Fabr

62798f6e 11/26/2019 01:40 PM tiamilani

intermidiate state

cfe4c6c3 11/12/2019 10:52 AM tiamilani

addresses bug fixed

fb99df25 11/12/2019 10:50 AM tiamilani

Possibilit√° di disabilitare l'mrai medio impostandolo a 0

0a478326 11/11/2019 08:32 AM tiamilani

possibility to plot multiple cdf on the same plot, implemented the mean_mrai on mrai_setter

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README


MRAI setter

This software takes in input a graphml annotated according to the specifications of IoF and set the node MRAI values.

Usage

./mrai_setter.py <graphml_file> <strategy> <outputDir> <mean_mrai> [<advertising_node>]

The output dir will be created if it does not exist

The optional option indicates which node is advertising. This can be of paramount importance for some strategies (e.g., Fabrikant gadgets).

Strategies

Strategies are MRAI setting policies. At the time of writing available strategies include: * 30secs: set all timers to 30 seconds * none: set all timers to 0 seconds * fabrikant: set timers according to worst case gadget configuration (see paper) * inversefabrikant: inverted timers of the previous case (should lead to good case) * dpc: set timers according to our theorecal derived model based on destination partial centrality (DPC) * dpc2: variation of the previous one, with a different normalization factor * uniformdistrmrai: Set timers randomly following a uniform distribution btween 'defaultmrai'%'percentageconstant' and defaultmrai * constantfabrikant: Set timers following Fabrikant policies, but with a constant increment, the constant percentage is given by 'percentageconstant' * constantinversefabrikant: Set timers following inverse Fabrikant polices, but with a constant decrement, the constant percentage is given by 'percentage_constant'

Tests

Just type python3 -mpytest

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