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
Franka Emika Panda manipulator kinematics&dynamics simulation

pybullet_sim_panda Pybullet simulation environment for Franka Emika Panda Dependency pybullet, numpy, spatial_math_mini Simple example (please check s

0 Jan 20, 2022
BlueFog Tutorials

BlueFog Tutorials Welcome to the BlueFog tutorials! In this repository, we've put together a collection of awesome Jupyter notebooks. These notebooks

4 Oct 27, 2021
A deep-learning pipeline for segmentation of ambiguous microscopic images.

Welcome to Official repository of deepflash2 - a deep-learning pipeline for segmentation of ambiguous microscopic images. Quick Start in 30 seconds se

Matthias Griebel 39 Dec 19, 2022
Official implementation of the paper "Topographic VAEs learn Equivariant Capsules"

Topographic Variational Autoencoder Paper: https://arxiv.org/abs/2109.01394 Getting Started Install requirements with Anaconda: conda env create -f en

T. Andy Keller 69 Dec 12, 2022
PanopticBEV - Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images This r

63 Dec 16, 2022
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll

7 Feb 10, 2022
Source code for paper: Knowledge Inheritance for Pre-trained Language Models

Knowledge-Inheritance Source code paper: Knowledge Inheritance for Pre-trained Language Models (preprint). The trained model parameters (in Fairseq fo

THUNLP 31 Nov 19, 2022
CLIP (Contrastive Language–Image Pre-training) trained on Indonesian data

CLIP-Indonesian CLIP (Radford et al., 2021) is a multimodal model that can connect images and text by training a vision encoder and a text encoder joi

Galuh 17 Mar 10, 2022
最新版本yolov5+deepsort目标检测和追踪，支持5.0版本可训练自己数据集

使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。

422 Dec 30, 2022
PyTorch implementation of TSception V2 using DEAP dataset

TSception This is the PyTorch implementation of TSception V2 using DEAP dataset in our paper: Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai

Yi Ding 27 Dec 15, 2022
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Autoformer (NeurIPS 2021) Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Time series forecasting is a c

THUML @ Tsinghua University 847 Jan 08, 2023
A Blender python script for getting asset browser custom preview images for objects and collections.

asset_snapshot A Blender python script for getting asset browser custom preview images for objects and collections. Installation: Click the code butto

Johnny Matthews 44 Nov 29, 2022
Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

Microsoft365_devicePhish Abusing Microsoft 365 OAuth Authorization Flow for Phishing Attack This is a simple proof-of-concept script that allows an at

Alex 236 Dec 21, 2022
The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography

James 135 Dec 23, 2022
Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021.

Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021. Figure 1: In the process of motion capture (mocap), some joints or even the whole human

Shinny cui 3 Oct 31, 2022
The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

TDANet: Text-Guided Neural Image Inpainting, MM'2020 (Oral) MM | ArXiv This repository implements the paper "Text-Guided Neural Image Inpainting" by L

LisaiZhang 75 Dec 22, 2022
Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows.

Swin-Transformer Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows. For more details, ple

旷视天元 MegEngine 9 Mar 14, 2022
Aydin is a user-friendly, feature-rich, and fast image denoising tool

Aydin is a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms.

Royer Lab 99 Dec 14, 2022
An atmospheric growth and evolution model based on the EVo degassing model and FastChem 2.0

EVolve Linking planetary mantles to atmospheric chemistry through volcanism using EVo and FastChem. Overview EVolve is a linked mantle degassing and a

Pip Liggins 2 Jan 17, 2022
Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

Regression Transformer Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression . Development se

International Business Machines 27 Jan 05, 2023