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
Super Pix Adv - Offical implemention of Robust Superpixel-Guided Attentional Adversarial Attack (CVPR2020)

Super_Pix_Adv Offical implemention of Robust Superpixel-Guided Attentional Adver

DLight 8 Oct 26, 2022
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees

Learning Efficient Online 3D Bin Packing on Packing Configuration Trees This repository is being continuously updated, please stay tuned! Any code con

86 Dec 28, 2022
Relaxed-machines - explorations in neuro-symbolic differentiable interpreters

Relaxed Machines Explorations in neuro-symbolic differentiable interpreters. Baby steps: inc_stop Libraries JAX Haiku Optax Resources Chapter 3 (∂4: A

Nada Amin 6 Feb 02, 2022
Zeyuan Chen, Yangchao Wang, Yang Yang and Dong Liu.

Principled S2R Dehazing This repository contains the official implementation for PSD Framework introduced in the following paper: PSD: Principled Synt

zychen 78 Dec 30, 2022
Simple object detection app with streamlit

object-detection-app Simple object detection app with streamlit. Upload an image and perform object detection. Adjust the confidence threshold to see

Robin Cole 68 Jan 02, 2023
The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

The accompanying codes reproduce all figures and statistics presented in "Controlling for multiple covariates" by Mark Tygert. This repository also pr

Meta Research 1 Dec 02, 2021
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae In our paper, we p

Rishikesh (ऋषिकेश) 31 Dec 08, 2022
RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems This is our implementation for the paper: Weibo Gao, Qi Liu*, Zhenya Hu

BigData Lab @USTC 中科大大数据实验室 10 Oct 16, 2022
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Python inverse kinematics for your robot model based on Pinocchio.

Python inverse kinematics for your robot model based on Pinocchio.

Stéphane Caron 50 Dec 22, 2022
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

19 Dec 10, 2022
Source code and data in paper "MDFEND: Multi-domain Fake News Detection (CIKM'21)"

MDFEND: Multi-domain Fake News Detection This is an official implementation for MDFEND: Multi-domain Fake News Detection which has been accepted by CI

Rich 40 Dec 18, 2022
KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

86 Dec 12, 2022
the code for paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration"

EOW-Softmax This code is for the paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration". Accepted by ICCV21. Usage Commnd exa

Yezhen Wang 36 Dec 02, 2022
TART - A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions

TART This project is a PyTorch implementation for Transition Matrix Representati

Lee Sael 2 Jan 19, 2022
Supervised forecasting of sequential data in Python.

Supervised forecasting of sequential data in Python. Intro Supervised forecasting is the machine learning task of making predictions for sequential da

The Alan Turing Institute 54 Nov 15, 2022
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

Pyserini Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse re

Castorini 706 Dec 29, 2022
Finetune SSL models for MOS prediction

Finetune SSL models for MOS prediction This is code for our paper under review for ICASSP 2022: "Generalization Ability of MOS Prediction Networks" Er

Yamagishi and Echizen Laboratories, National Institute of Informatics 32 Nov 22, 2022
A library for uncertainty quantification based on PyTorch

Torchuq [logo here] TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch. TorchUQ currently supports 10 representation

TorchUQ 96 Dec 12, 2022
PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.

Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp

22 Dec 02, 2022