iof-tools / mrai_setter @ cfe4c6c3
|Readme.md||1.47 KB||0a478326||4 months||tiamilani||possibility to plot multiple cdf on the same pl...|
|milaniBGPLoad.py||4.86 KB||c1397947||4 months||tiamilani||corrected DPC calculation, based on which node ...|
|mrai_setter.py||11.4 KB||cfe4c6c3||4 months||tiamilani||addresses bug fixed|
|test_mrai_setter.py||8.22 KB||28c0971b||4 months||tiamilani||new mrai settings, test done|
This software takes in input a graphml annotated according to the specifications of IoF and set the node MRAI values.
./mrai_setter.py <graphml_file> <strategy> <outputDir> <mean_mrai> [<advertising_node>]
The output dir will be created if it does not exist
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) * milanicent: set timers according to our theorecal derived model based on Milani centrality (mice) * milanicent2: 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'
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