[ndnSIM] [EXT] Neural networks in ndnSIM

Eric Binnendyk eric.binnendyk at student.nmt.edu
Sat May 8 12:52:43 PDT 2021

Hi Junxiao,

Thanks so much for the reply. It really clears up some questions I had
about how to use ndnSIM. I'll probably implement it the asynchronous way
and have the C++ and Python code communicate using files.


On Sat, May 8, 2021 at 7:24 AM Junxiao Shi <shijunxiao at email.arizona.edu>

> Hi Eric
>> I'm trying to implement a neural network to do forwarding in ndnSIM. I
>> know this is possible to do in ndnSIM because these papers do it:
>> https://www.mdpi.com/1424-8220/18/10/3354/htm,
>> https://dl.acm.org/doi/pdf/10.1145/3229543.3229547.
>> Does anyone have any recommendations on how to implement a neural
>> network? If possible, I would like to do it in Python even though most of
>> the ndnSIM code is in C++. Is it possible to use the Python bindings to
>> write a forwarding strategy in Python, so that I can use a neural network
>> library like keras?
> Forwarding strategy is normally a C++ class that is called synchronously.
> C++ code can invoke Python synchronously.
> Implementing part of the forwarding strategy logic in Python wouldn't be
> easy or performant, but I don't see major roadblock.
> It would not be a "binding" though, because a "binding" typically refers
> to calling C++ code from Python, not the other way around.
> Another possibility is performing machine learning asynchronously.
> In this approach, the forwarding strategy (C++) and the neural network
> (Python) share a common data structure, but do not interact with each other
> on a per packet basis.
> The forwarding strategy makes immediate decisions based on what's already
> in the data structure, and stores the outcome (e.g. satisfy ratio and RTT)
> into the data structure.
> The neural network periodically reads from the date structure, does its
> magic, and writes to the data structure to influence future forwarding
> decisions.
> Yours, Junxiao
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