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
这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download 训练步骤

Bubbliiiing 350 Dec 28, 2022
Code for paper: Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

Group-CAM By Zhang, Qinglong and Rao, Lu and Yang, Yubin [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the o

zhql 98 Nov 16, 2022
Empower Sequence Labeling with Task-Aware Language Model

LM-LSTM-CRF Check Our New NER Toolkit 🚀 🚀 🚀 Inference: LightNER: inference w. models pre-trained / trained w. any following tools, efficiently. Tra

Liyuan Liu 838 Jan 05, 2023
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI 2022)

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

Wei-Yao Wang 11 Nov 30, 2022
code for paper"A High-precision Semantic Segmentation Method Combining Adversarial Learning and Attention Mechanism"

PyTorch implementation of UAGAN(U-net Attention Generative Adversarial Networks) This repository contains the source code for the paper "A High-precis

Tong 8 Apr 25, 2022
Official code repository for the publication "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons"

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons This repository contains the code to repr

Computational Neuroscience, University of Bern 3 Aug 04, 2022
Liver segmentation using MONAI and pytorch

Machine Learning use case in the field of Healthcare. In this project MONAI and pytorch frameworks are used for 3D Liver segmentation.

Abhishek Gajbhiye 2 May 30, 2022
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
Highly comparative time-series analysis

〰️ hctsa 〰️ : highly comparative time-series analysis hctsa is a software package for running highly comparative time-series analysis using Matlab (fu

Ben Fulcher 569 Dec 21, 2022
fcn by tensorflow

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

9 May 22, 2022
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le

deargen 11 Nov 19, 2022
Implementation for Learning to Track with Object Permanence

Learning to Track with Object Permanence A video-based MOT approach capable of tracking through full occlusions: Learning to Track with Object Permane

Toyota Research Institute - Machine Learning 91 Jan 03, 2023
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

Princeton Natural Language Processing 119 Sep 28, 2022
CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.

CenterFace Introduce CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices. Recent Update 2019.09.

StarClouds 1.2k Dec 21, 2022
A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

Image Classification in Python Implementing image classification in Flask using Keras. The VGG16 is a convolution neural network model architecture th

Gerald Maduabuchi 19 Dec 12, 2022
Cascaded Pyramid Network (CPN) based on Keras (Tensorflow backend)

ML2 Takehome Project Reimplementing the paper: Cascaded Pyramid Network for Multi-Person Pose Estimation Dataset The model uses the COCO dataset which

Vo Van Tu 1 Nov 22, 2021
Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning

RIIT Our open-source code for RIIT: Rethinking the Importance of Implementation Tricks in Multi-AgentReinforcement Learning. We implement and standard

405 Jan 06, 2023
This project implements "virtual speed" from heart rate monito

ANT+ Virtual Stride Based Speed and Distance Monitor Overview This project imple

2 May 20, 2022
Corgis are the cutest creatures; have 30K of them!

corgi-net This is a dataset of corgi images scraped from the corgi subreddit. After filtering using an ImageNet classifier, the training set consists

Alex Nichol 6 Dec 24, 2022
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

DV Lab 137 Dec 14, 2022