Dear All,
--
Thanks in advance. I am a PhD candidate, and currently I have started working on kernel development. My professor told me to implement this paper. In this paper authors have used machine learning to predict the next missed addresses.They used instruction addresses (program counters PCs), and corresponding missed addresses as features to train the machine learning classifier.
As a kernel newbie, I don't know how to initiate the implementation. I am thinking to implement it in the following steps:
1- Saving instruction addresses (PCs), and corresponding missed addresses
2- Perform the machine learning
3-Do the prediction using machine learning classifier, and swap in/swap out the pages.
I have some questions that
1) How can I directly store the missed addresses, and instruction addresses from kernel handle_mm_fault() to a file?
2) How can I use machine learning classifier in the kernel for predicting addresses?
3) Is there any way to do the machine learning in the user space in python, and then transfer the classifier in bytes forms to the kernel space for address predictions ?
Currently, I am stuck in getting the missed addresses by storing it into the file. I am trying to send the missed addresses to the user space from the kernel space, and store it into the file. I know that /proc, ioctl, and netlink are some options to exchange data between kernel and user spaces. I have selected the netlink, and trying to customize this code for my task. I need some type of communication API which let me do exchange anytype of data (integers, string, bytes, and may be structures to bind all these types of data) between kernel space, and user space.
Any type of help, and guidelines will be highly appreciated.
Thank you.
Best Regards,
Mr. Irfan Ullah
PhD Candidate
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sahibzada_irfanullah
Data and Knowledge Engineering(DKE) Lab
Department of Computer Science and Engineering
Kyung Hee University, South Korea.
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