https://github.com/snowzjx/liteflow
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
https://github.com/snowzjx/liteflow
datapath kernel kernel-module networking neural-networks
Last synced: 21 days ago
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A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
- Host: GitHub
- URL: https://github.com/snowzjx/liteflow
- Owner: snowzjx
- License: gpl-2.0
- Created: 2020-02-10T04:28:44.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2023-05-15T09:11:17.000Z (almost 3 years ago)
- Last Synced: 2024-12-05T01:15:13.936Z (over 1 year ago)
- Topics: datapath, kernel, kernel-module, networking, neural-networks
- Language: C
- Homepage:
- Size: 476 KB
- Stars: 25
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
[](https://travis-ci.org/liteflow/liteflow)
Please refer to our [SIGCOMM '22 paper](https://dl.acm.org/doi/10.1145/3544216.3544229) for details.
**ATTENTION:** This project is for research purposes and please do not use it in a production environment. **Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon.** However, you can still experience some of the key components mentioned in our paper.
**Tested Kernel Version:** 4.15.0-173-generic
## Getting Started
### Installation
1. Clone the repo
```sh
git clone https://github.com/snowzjx/liteflow.git
```
1. Change the directory to LiteFlow
```sh
cd liteflow
```
1. Install required packages
```sh
apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip
```
```sh
pip3 install -U pip
pip3 install -r bin/requirements.txt
```
1. Compile
```sh
make all
```
1. Install LiteFlow kernel module
```sh
make module_install
```
You can check if the LiteFlow kernel module is successfully installed by executing ```lsmod``` to see if *lf_kernel* is there.
1. Install TCP Congestion Control module
```sh
make tcp_kernel_install
```
You can check if the LiteFlow TCP Congestion Control is successfully installed by executing ```lsmod``` to see if *lf_tcp_kernel* is there.
You can also check if the TCP Congestion Control is successfully activated by executing ```sysctl net.ipv4.tcp_congestion_control``` to see whether is value is *lf_tcp_kernel*
### Examples
#### Snapshot Generation
```sh
cd script
./snapshot_generation.sh
```
This command tries to generate 3 Aurora snapshots from the model in ```liteflow/data/```. You can verify if the command is successful by checking if *lf_model_1.ko*, *lf_model_2.ko*, and *lf_model_3.ko* are generated. You can also check the generated source code by looking into *lf_model_1.c*, *lf_model_2.c*, and *lf_model_3.ko* files. Note here, we generate 3 identical snapshots just for demo purposes.
#### Snapshot Update
```sh
cd script
./active_standby_switch.sh
```
You expect a *dmesg* output showing that LiteFlow uses different neural networks to serve different flows.
```sh
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
```
#### Batched Training Data Delivery
```sh
cd script
./fetch_data_from_kernel.sh
```
The screen should print all training data. *Note: different kernel settings may cause different outputs, please change the code accordingly.*
#### LiteFlow Userspace Demo
```sh
cd bin
python3 lf.py
```
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. *Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...*
## Citation
```bibtex
@inproceedings{liteflow,
author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen},
title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath},
year = {2022},
booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference},
}
```