Machine Learning Framework for Operating Systems - Brings ML to Linux kernel

Last update: Nov 24, 2022

Overview

KML: A Machine Learning Framework for Operating Systems & Storage Systems

Storage systems and their OS components are designed to accommodate a wide variety of applications and dynamic workloads. Storage components inside the OS contain various heuristic algorithms to provide high performance and adaptability for different workloads. These heuristics may be tunable via parameters, and some system calls allow users to optimize their system performance. These parameters are often predetermined based on experiments with limited applications and hardware. Thus, storage systems often run with these predetermined and possibly suboptimal values. Tuning these parameters manually is impractical: one needs an adaptive, intelligent system to handle dynamic and complex workloads. Machine learning (ML) techniques are capable of recognizing patterns, abstracting them, and making predictions on new data. ML can be a key component to optimize and adapt storage systems. We propose KML, an ML framework for operating systems & storage systems. We implemented a prototype and demonstrated its capabilities on the well-known problem of tuning optimal readahead values. Our results show that KML has a small memory footprint, introduces negligible overhead, and yet enhances throughput by as much as 2.3×.

For more information on the KML project, please see our papers

KML is under development by Ibrahim Umit Akgun of the File Systems and Storage Lab (FSL) at Stony Brook University under Professor Erez Zadok.

Setup
Example
Design
Citing KML

Setup

Clone KML

# SSH
git clone --recurse-submodules [email protected]:sbu-fsl/kernel-ml.git

# HTTPS
git clone --recurse-submodules https://github.com/sbu-fsl/kernel-ml.git

Build Dependencies

KML depends on the following third-party repositories:

# Create and enter a directory for dependencies
mkdir dependencies
cd dependencies

# Clone repositories
git clone https://github.com/google/benchmark.git
git clone https://github.com/google/googletest.git

# Build google/benchmark
cd benchmark
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ../
make
sudo make install

# Build google/googletest
cd ../googletest
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ../
make
sudo make install
cd ../..

Install KML Linux Kernel Modifications

KML requires Linux kernel modifications to function. We recommend allocating at least 25 GiB of disk space before beginning the installation process.

Navigate to the kernel-ml/kernel-ml-linux directory. This repository was recursively cloned during setup
```
cd kernel-ml-linux
```

Install the following packages

git fakeroot build-essential ncurses-dev xz-utils libssl-dev bc flex libelf-dev bison

Install the modified kernel as normal. No changes are required for make menuconfig

cp /boot/config-$(uname -r) .config
make menuconfig
make -j$(nproc)
sudo make modules_install -j$(nproc)
sudo make install -j$(nproc)

Restart your machine
```
sudo reboot
```
Confirm that you now have Linux version 4.19.51+ installed
```
uname -a
```

Specify Kernel Header Location

Edit kernel-ml/cmake/FindKernelHeaders.cmake to specify the absolute path to the aforementioned kernel-ml/kernel-ml-linux directory. For example, if kernel-ml-linux lives in /home/kernel-ml/kernel-ml-linux:

...

# Find the headers
find_path(KERNELHEADERS_DIR
        include/linux/user.h
        PATHS /home/kernel-ml/kernel-ml-linux
)

...

Build KML

# Create a build directory for KML
mkdir build
cd build 
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-Werror" ..
make

Double Check

In order to check everything is OK, we can run tests and benchmarks.

cd build
ctest --verbose

Design

Example

Citing KML

To cite this repository:

@TECHREPORT{umit21kml-tr,
  AUTHOR =       "Ibrahim Umit Akgun and Ali Selman Aydin and Aadil Shaikh and Lukas Velikov and Andrew Burford and Michael McNeill and Michael Arkhangelskiy and Erez Zadok",
  TITLE =        "KML: Using Machine Learning to Improve Storage Systems",
  INSTITUTION =  "Computer Science Department, Stony Brook University",
  YEAR =         "2021",
  MONTH =        "Nov",
  NUMBER =       "FSL-21-02",
}

@INPROCEEDINGS{hotstorage21kml,
  TITLE =        "A Machine Learning Framework to Improve Storage System Performance",
  AUTHOR =       "Ibrahim 'Umit' Akgun and Ali Selman Aydin and Aadil Shaikh and Lukas Velikov and Erez Zadok",
  NOTE =         "To appear",
  BOOKTITLE =    "HotStorage '21: Proceedings of the 13th ACM Workshop on Hot Topics in Storage",
  MONTH =        "July",
  YEAR =         "2021",
  PUBLISHER =    "ACM",
  ADDRESS =      "Virtual",
  KEY =          "HOTSTORAGE 2021",
}

Self-Supervised Learning with Kernel Dependence Maximization

Self-Supervised Learning with Kernel Dependence Maximization This is the code for SSL-HSIC, a self-supervised learning loss proposed in the paper Self

29 Dec 29, 2022

⚡ Fast • 🪶 Lightweight • 0️⃣ Dependency • 🔌 Pluggable • 😈 TLS interception • 🔒 DNS-over-HTTPS • 🔥 Poor Man's VPN • ⏪ Reverse & ⏩ Forward • 👮🏿 "Proxy Server" framework • 🌐 "Web Server" framework • ➵ ➶ ➷ ➠ "PubSub" framework • 👷 "Work" acceptor & executor framework

Table of Contents Features Install Using PIP Stable version Development version Using Docker Stable version Development version Using HomeBrew Stable

2.2k Jan 8, 2023

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Machine Learning From Scratch About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose

21.8k Jan 9, 2023

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin

8.1k Jan 6, 2023

Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021) This repository is the official PyTorc

139 Dec 29, 2022

Machine Learning Framework for Operating Systems - Brings ML to Linux kernel

Related tags

Overview

KML: A Machine Learning Framework for Operating Systems & Storage Systems

Table of Contents

Setup

Clone KML

Build Dependencies

Install KML Linux Kernel Modifications

Specify Kernel Header Location

Build KML

Double Check

Design

Example

Citing KML

You might also like...

Self-Supervised Learning with Kernel Dependence Maximization

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Code for Mesh Convolution Using a Learned Kernel Basis

(CVPR 2021) PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

Exploring Image Deblurring via Blur Kernel Space (CVPR'21)

tinykernel - A minimal Python kernel so you can run Python in your Python

Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Releases(v0.0.1)

v0.0.1(Nov 12, 2021)

Owner

File systems and Storage Lab (FSL)

This repository is the code of the paper Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

Detectron2-FC a fast construction platform of neural network algorithm based on detectron2

Automatic Number Plate Recognition using Contours and Convolution Neural Networks (CNN)

PyTorch implementation HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections

Hyperbolic Hierarchical Clustering.

Quantized tflite models for ailia TFLite Runtime

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

Benchmarking Pipeline for Prediction of Protein-Protein Interactions

A deep learning network built with TensorFlow and Keras to classify gender and estimate age.

Pose estimation with MoveNet Lightning

Repository for the electrical and ICT benchmark model developed in the ERIGrid 2.0 project.

A knowledge base construction engine for richly formatted data

Python program that works as a contact list

Multivariate Boosted TRee

A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Simple torch.nn.module implementation of Alias-Free-GAN style filter and resample

Code for Transformer Hawkes Process, ICML 2020.

Product-based-recommendation-system - A product based recommendation system which uses Machine learning algorithm such as KNN and cosine similarity

FAMIE is a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction (IE)