The code for the NSDI'21 paper "BMC: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing".

Overview

BMC

The code for the NSDI'21 paper "BMC: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing".

BibTex entry available here.

BMC (BPF Memory Cache) is an in-kernel cache for memcached. It enables runtime, crash-safe extension of the Linux kernel to process specific memcached requests before the execution of the standard network stack. BMC does not require modification of neither the Linux kernel nor the memcached application. Running memcached with BMC improves throughput by up to 18x compared to the vanilla memcached application.

Requirements

Linux kernel v5.3 or higher is required to run BMC.

Other software dependencies are required to build BMC and Memcached-SR (see Building BMC and Building Memcached-SR).

Build instructions

Building BMC

BMC must be compiled with libbpf and other header files obtained from kernel sources. The project does not include the kernel sources, but the kernel-src-download.sh and kernel-src-prepare.sh scripts automate the download of the kernel sources and prepare them for the compilation of BMC.

These scripts require the following software to be installed:

gpg curl tar xz make gcc flex bison libssl-dev libelf-dev

The project uses llvm and clang version 9 to build BMC, but more recent versions might work as well:

llvm-9 clang-9

Note that libelf-dev is also required to build libbpf and BMC.

With the previous software installed, BMC can be built with the following:

$ ./kernel-src-download.sh
$ ./kernel-src-prepare.sh
$ cd bmc && make

After BMC has been successfully built, kernel sources can be removed by running the kernel-src-remove.sh script from the project root.

Building Memcached-SR

Memcached-SR is based on memcached v1.5.19. Building it requires the following software:

clang-9 (or gcc-9) automake libevent-dev

Either clang-9 or gcc-9 is required in order to compile memcached without linking issues. Depending on your distribution, you might also need to use the -Wno-deprecated-declarations compilation flag.

Memcached-SR can be built with the following:

$ cd memcached-sr 
$ ./autogen.sh
$ CC=clang-9 CFLAGS='-DREUSEPORT_OPT=1 -Wno-deprecated-declarations' ./configure && make

The memcached binary will be located in the memcached-sr directory.

Further instructions

TC egress hook

BMC doesn't attach the tx_filter eBPF program to the egress hook of TC, it needs to be attached manually.

To do so, you first need to make sure that the BPF is mounted, if it isn't you can mount it with the following command:

# mount -t bpf none /sys/fs/bpf/

Once BMC is running and the tx_filter program has been pinned to /sys/fs/bpf/bmc_tx_filter, you can attach it using the tc command line:

# tc qdisc add dev 
   
     clsact
   
# tc filter add dev 
   
     egress bpf object-pinned /sys/fs/bpf/bmc_tx_filter
   

After you are done using BMC, you can detach the program with these commands:

# tc filter del dev 
   
     egress
   
# tc qdisc del dev 
   
     clsact
   

And unpin the program with # rm /sys/fs/bpf/bmc_tx_filter

License

Files under the bmc directory are licensed under the GNU Lesser General Public License version 2.1.

Files under the memcached-sr directory are licensed under the BSD-3-Clause BSD license.

Cite this work

BibTex:

@inproceedings{265047,
	title        = {{BMC}: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing},
	author       = {Yoann Ghigoff and Julien Sopena and Kahina Lazri and Antoine Blin and Gilles Muller},
	year         = 2021,
	month        = apr,
	booktitle    = {18th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 21)},
	publisher    = {{USENIX} Association},
	pages        = {487--501},
	isbn         = {978-1-939133-21-2},
	url          = {https://www.usenix.org/conference/nsdi21/presentation/ghigoff}
}
Owner
Orange
Open Source by Orange
Orange
SPRING is a seq2seq model for Text-to-AMR and AMR-to-Text (AAAI2021).

SPRING This is the repo for SPRING (Symmetric ParsIng aNd Generation), a novel approach to semantic parsing and generation, presented at AAAI 2021. Wi

Sapienza NLP group 98 Dec 21, 2022
GAN-based Matrix Factorization for Recommender Systems

GAN-based Matrix Factorization for Recommender Systems This repository contains the datasets' splits, the source code of the experiments and their res

Ervin Dervishaj 9 Nov 06, 2022
iBOT: Image BERT Pre-Training with Online Tokenizer

Image BERT Pre-Training with iBOT Official PyTorch implementation and pretrained models for paper iBOT: Image BERT Pre-Training with Online Tokenizer.

Bytedance Inc. 435 Jan 06, 2023
Python utility to generate filesystem content for Obsidian.

Security Vault Generator Quickly parse, format, and output common frameworks/content for Obsidian.md. There is a strong focus on MITRE ATT&CK because

Justin Angel 73 Dec 02, 2022
PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

PASTRIE Official release of the corpus described in the paper: Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, and Nathan Schn

NERT @ Georgetown 4 Dec 02, 2021
Async API for controlling Hue Lights

Hue API Async API for controlling Hue Lights Documentation: hue-api.nirantak.com Source: github.com/nirantak/hue-api Installation This is an async cli

Nirantak Raghav 4 Nov 16, 2022
RID-Noise: Towards Robust Inverse Design under Noisy Environments

This is code of RID-Noise. Reproduce RID-Noise Results Toy tasks Please refer to the notebook ridnoise.ipynb to view experiments on three toy tasks. B

Thyrix 2 Nov 23, 2022
An unopinionated replacement for PyTorch's Dataset and ImageFolder, that handles Tar archives

Simple Tar Dataset An unopinionated replacement for PyTorch's Dataset and ImageFolder classes, for datasets stored as uncompressed Tar archives. Just

Joao Henriques 47 Dec 20, 2022
Implementation and replication of ProGen, Language Modeling for Protein Generation, in Jax

ProGen - (wip) Implementation and replication of ProGen, Language Modeling for Protein Generation, in Pytorch and Jax (the weights will be made easily

Phil Wang 71 Dec 01, 2022
Using VapourSynth with super resolution models and speeding them up with TensorRT.

VSGAN-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Using NVIDIA/Torch-TensorRT combined wi

111 Jan 05, 2023
Using LSTM to detect spoofing attacks in an Air-Ground network

Using LSTM to detect spoofing attacks in an Air-Ground network Specifications IDE: Spider Packages: Tensorflow 2.1.0 Keras NumPy Scikit-learn Matplotl

Tiep M. H. 1 Nov 20, 2021
Applications using the GTN library and code to reproduce experiments in "Differentiable Weighted Finite-State Transducers"

gtn_applications An applications library using GTN. Current examples include: Offline handwriting recognition Automatic speech recognition Installing

Facebook Research 68 Dec 29, 2022
Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN Introduction Image super-resolution (SR) is the process of recovering high-resoluti

8 Apr 15, 2022
A 10000+ hours dataset for Chinese speech recognition

WenetSpeech Official website | Paper A 10000+ Hours Multi-domain Chinese Corpus for Speech Recognition Download Please visit the official website, rea

310 Jan 03, 2023
PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking

Homography Decomposition Networks for Planar Object Tracking This project is the offical PyTorch implementation of HDN(Homography Decomposition Networ

CaptainHook 48 Dec 15, 2022
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching This repository contains the source code for our paper: RAFT-Stereo: Multilevel

Princeton Vision & Learning Lab 328 Jan 09, 2023
Code for paper: "Spinning Language Models for Propaganda-As-A-Service"

Spinning Language Models for Propaganda-As-A-Service This is the source code for the Arxiv version of the paper. You can use this Google Colab to expl

Eugene Bagdasaryan 16 Jan 03, 2023
[ArXiv 2021] One-Shot Generative Domain Adaptation

GenDA - One-Shot Generative Domain Adaptation One-Shot Generative Domain Adaptation Ceyuan Yang*, Yujun Shen*, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Z

GenForce: May Generative Force Be with You 46 Dec 19, 2022
Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

Weakly- and Semi-Supervised Panoptic Segmentation by Qizhu Li*, Anurag Arnab*, Philip H.S. Torr This repository demonstrates the weakly supervised gro

Qizhu Li 159 Dec 20, 2022
FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS

FaceAPI AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using

Vladimir Mandic 395 Dec 29, 2022