Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

Related tags

Deep LearningAquarius
Overview

Aquarius

Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

NOTE: We are currently going through the open-source process required by our institution. The content will soon be available. The steps that need to be completed are listed below:

  • PREPARE
  • INCLUSIVELINT
  • UNITTEST
  • LINT
  • BUILD & PUBLISH
  • CORONA
  • BLACKDUCK
  • SONARQUBE
  • HELM
  • DEPLOY
  • DEPLOY-STATIC
  • E2E
  • APIDOCS
  • GOPUBLISH

Introduction

This repository implements a data-collection and data-exploitation mechanism Aquarius as a load balancer plugin in VPP. For the sake of reproducibility, software and data artifacts for performance evaluation are maintained in this repository.

Directory Roadmap

- config                    // configuration files in json format        
- sc-author-kit-log         // artifacts description of testbed hardware, required by sc21 committee
- src                       // source code
    + client/server         // scripts that run on client/server VMs
    + lb                    // scripts that run on lb VMs
        * dev               // dev version (for offline feature collection)
        * deploy            // deploy version (for online policy evaluation)
    + utils                 // utility scripts that help to run the testbed
    + vpp                   // vpp plugin
        * dev               // dev version (for offline feature collection)
        * deploy            // deploy version (for online policy evaluation)
    + test                  // unit test codes
- data                      
    + trace                 // network traces replayed on the testbed
    + results (omitted)     // This is where all the datasets are dumped (will be automatically created once we run experiments)
    + img                   // VM image files (omitted here because of file size, server configurations are documented in README)
    + vpp_deb               // stores deb files for installing VPP on VMs
        * dev               // dev version (for offline feature collection)
        * deploy            // deploy version (for online policy evaluation)

Get Started

Pre-Configuration

Run python3 setup.py, which does the following things:

  • update the root directory in config/global_config.json to the directory of the cloned aquarius repository (replace the /home/yzy/aquarius);
  • clone the VPP repository in src/vpp/base;
  • update the physical_server_ip in config/global_config.json to the IP addresses of the actual server IP addresses in use;
  • update the vlan_if as the last network interface on the local machine
  • update the physical_server_ip in config/cluster/unittest-1.json to the local hostname

VM images

To prepare a VM original image, refer to the README file in data. To run all the experiments without issues, create a ssh-key on the host servers and copy the public key to the VMs so that commands can be executed from the host using ssh -t -t.

Run example

A simple example is created using a small network topology (1 client, 1 edge router, 1 load balancer, and 4 application servers) on a single machine. Simply follow the jupyter notebook in notebook/unittest. Make sure the configurations are well adapted to your own host machine. Also make sure that the host machine has at least 20 CPUs. Otherwise, the configuration can be modified in config/cluster/unittest-1.json. To reduce the amount of CPUs required, change the number of vcpu of each node in the json file.

Reproducibility

To reproduce the results in Aquarius paper, three notebooks are presented in notebook/reproduce. The dataset that are generated from the experiments are stored in data/reproduce. To run these experiments, 4 physical machines with 12 physcial cores (48 CPUs) each are required. MACROs in the notebook should be well adapted. For instance, VLAN should be configured across the actual inerfacesin use. An example of network topology is depicted below.

Multi-server Topology

Notes

Running the scripts, e.g. src/utils/testbed_utils.py, requires root access.

Aquarius

Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

You might also like...
Official code for ICCV2021 paper
Official code for ICCV2021 paper "M3D-VTON: A Monocular-to-3D Virtual Try-on Network"

M3D-VTON: A Monocular-to-3D Virtual Try-On Network Official code for ICCV2021 paper "M3D-VTON: A Monocular-to-3D Virtual Try-on Network" Paper | Suppl

null 109 Dec 29, 2022
Official PyTorch implementation of
Official PyTorch implementation of "RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on" (IJCAI-ECAI 2022)

RMGN-VITON RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on In IJCAI-ECAI 2022(short oral). [Paper] [Supplementary Material] Abstra

null 27 Dec 1, 2022
Code of paper
Code of paper "CDFI: Compression-Driven Network Design for Frame Interpolation", CVPR 2021

CDFI (Compression-Driven-Frame-Interpolation) [Paper] (Coming soon...) | [arXiv] Tianyu Ding*, Luming Liang*, Zhihui Zhu, Ilya Zharkov IEEE Conference

Tianyu Ding 95 Dec 4, 2022
InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Men

Hong Wang 4 Dec 27, 2022
AI-based, context-driven network device ranking
AI-based, context-driven network device ranking

Batea A batea is a large shallow pan of wood or iron traditionally used by gold prospectors for washing sand and gravel to recover gold nuggets. Batea

Secureworks Taegis VDR 269 Nov 26, 2022
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022
A PyTorch Implementation of
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

Scalable Incomplete Network Embedding ⠀⠀ A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network em

Benedek Rozemberczki 69 Sep 22, 2022
The AugNet Python module contains functions for the fast computation of image similarity.
The AugNet Python module contains functions for the fast computation of image similarity.

AugNet AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation arxiv link In our work, we propose AugNet, a new deep le

Ming 74 Dec 28, 2022
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

Benedek Rozemberczki 188 Dec 29, 2022
Releases(sc22-v1.0-alpha)

  • sc22-v1.0-alpha(Jun 11, 2022)

    ALPHA version of Aquarius release for SC22.

    This release aims at demonstrating the basic workflow of the artifacts of Aquarius. Besides the jupyter notebooks which documents the actual procedure of producing all the experimental results in the paper, a unittest is provided to guide you through the basic workflow of the artifact.

    Please refer to the latest main branch of the Github repo to reproduce the core results presented in the paper: https://github.com/ZhiyuanYaoJ/Aquarius

    Source code(tar.gz)
    Source code(zip)
Owner
Zhiyuan YAO
PhD student at L'Ecole Polytechnique.
Zhiyuan YAO
GitHub Repository
shufflev2-yolov5：lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang 1.2k Dec 29, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022
A developer interface for creating Chat AIs for the Chai app.

ChaiPy A developer interface for creating Chat AIs for the Chai app. Usage Local development A quick start guide is available here, with a minimal exa

Chai 28 Dec 28, 2022
Goal of the project : Detecting Temporal Boundaries in Sign Language videos

MVA RecVis course final project : Goal of the project : Detecting Temporal Boundaries in Sign Language videos. Sign language automatic indexing is an

Loubna Ben Allal 6 Dec 21, 2022
Learned Token Pruning for Transformers

LTP: Learned Token Pruning for Transformers Check our paper for more details. Installation We follow the same installation procedure as the original H

Sehoon Kim 52 Dec 29, 2022
Official implementation of the ICCV 2021 paper: "The Power of Points for Modeling Humans in Clothing".

The Power of Points for Modeling Humans in Clothing (ICCV 2021) This repository contains the official PyTorch implementation of the ICCV 2021 paper: T

Qianli Ma 158 Nov 24, 2022
Official repository of DeMFI (arXiv.)

DeMFI This is the official repository of DeMFI (Deep Joint Deblurring and Multi-Frame Interpolation). [ArXiv_ver.] Coming Soon. Reference Jihyong Oh a

Jihyong Oh 56 Dec 14, 2022
A privacy-focused, intelligent security camera system.

Self-Hosted Home Security Camera System A privacy-focused, intelligent security camera system. Features: Multi-camera support w/ minimal configuration

Scott Barnes 175 Jan 01, 2023
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into tables through jointly extracting intervention, outcome and outcome measure entities and their relations.

Randomised controlled trial abstract result tabulator RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into

2 Sep 16, 2022
TUPÃ was developed to analyze electric field properties in molecular simulations

TUPÃ: Electric field analyses for molecular simulations What is TUPÃ? TUPÃ (pronounced as tu-pan) is a python algorithm that employs MDAnalysis engine

Marcelo D. Polêto 10 Jul 17, 2022
Patches desktop steam to look like the new steamdeck ui.

steam_deck_ui_patch The Deck UI patch will patch the regular desktop steam to look like the brand new SteamDeck UI. This patch tool currently works on

The_IT_Dude 3 Aug 29, 2022
PyTorch implementation of the paper Deep Networks from the Principle of Rate Reduction

Deep Networks from the Principle of Rate Reduction This repository is the official PyTorch implementation of the paper Deep Networks from the Principl

459 Dec 27, 2022
PyTorch implementation of our ICCV 2019 paper: Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

Impersonator PyTorch implementation of our ICCV 2019 paper: Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer an

SVIP Lab 1.7k Jan 06, 2023
Minimal diffusion models - Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)

Minimal code and simple experiments to play with Denoising Diffusion Probabilist

Rithesh Kumar 16 Oct 06, 2022
Official Implementation of "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras"

Multi Camera Pig Tracking Official Implementation of Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras CVPR2021 CV4Animals Workshop P

44 Jan 06, 2023
Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

PyLabel pip install pylabel PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. I

PyLabel Project 176 Jan 01, 2023
Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

Learning Category-Specific Mesh Reconstruction from Image Collections Angjoo Kanazawa*, Shubham Tulsiani*, Alexei A. Efros, Jitendra Malik University

438 Dec 22, 2022
The comma.ai Calibration Challenge!

Welcome to the comma.ai Calibration Challenge! Your goal is to predict the direction of travel (in camera frame) from provided dashcam video. This rep

comma.ai 697 Jan 05, 2023