Overview

SWARM is a wait-free replication protocol for shared data in disaggregated memory that provides 1-RTT reads and writes.

SWARM-KV is an RDMA-based disaggregated key-value store that leverages SWARM to offer 1-RTT GETs and UPDATEs.

This repository contains the artifacts and the instructions needed to reproduce the experiments in our SOSP paper. More precisely, it contains:

• Instructions on how to configure a cluster to deploy and run the experiments.
• Instructions on how to build and deploy the payloads of the experiments.
• Instructions on how to launch the experiments and obtain the results.

Claims

By running the experiments, you should be able to reproduce the numbers shown in:

• Figure 5: Latency CDFs for SWARM-KV, two other key-value stores (DM-ABD and FUSEE), and raw disaggregated memory.
• Figure 6: Per-client throughput-latency graphs for SWARM-KV and DM-ABD.
• Figure 7: Throughput and latency of SWARM-KV and DM-ABD for varying numbers of clients and parallel operations.
• Figure 8: Throughput and latency of SWARM-KV for YCSB workloads A and B, and varying value sizes; compared with a variant of SWARM-KV without in-place updates.
• Figure 9: Median latency and per-client throughput of SWARM-KV and DM-ABD for different numbers of replicas.
• Figure 10: Latency and throughput of a SWARM-KV client before and after the failure of a memory node.
• Figure 11: Latency CDFs for SWARM-KV and DM-ABD with a single key-value pair under stress and 16 clients.
• Figure 12: Latency CDFs for SWARM-KV for 64 clients and a varying number of metadata buffers.

Getting Started Instructions

Assuming you have access to a pre-configured cluster, you will be able to run a first experiment---that measures the latency of GETs and UPDATEs of SWARM-KV, two other key-value stores, and raw disaggregated memory (figure 5)---in less than 30 minutes by:

1. Connecting to the pre-configured cluster's gateway,
2. Building and deploying the evaluation binaries,
3. Running the scripts for figure 5.

Detailed instructions

This section will guide you on how to configure, build, and run all the experiments from scratch. If you have access to a pre-configured cluster, skip to building and deploying the binaries.

Cluster Configuration

Cluster Prerequisites

Running all experiments requires:

• a cluster of 8 machines connected via an InfiniBand fabric,
• Ubuntu 20.04 (different systems may work, but they have not been tested),
• all machines having the following ports open: 7000-7100, 11211, 18515, 9998.

Deployment Dependencies

Gateway Dependencies

The artifacts are built and packaged into binaries. Subsequently, these binaries are deployed from a gateway machine (e.g., your laptop). The gateway machine requires the following depencencies installed to be able to execute the deployment (and evaluation) scripts:

sudo apt install -y coreutils gawk python3 zip tmux

Optionnally, if you want to generate the plots from the datapoints, the gateway also requires the following dependencies:

sudo apt install python3-packaging fonts-linuxlibertine
fc-cache -f -v
rm ~/.cache/matplotlib -rf
pip3 install --upgrade pip
pip3 install --upgrade importlib_resources matplotlib

Cluster Machine Dependencies

The cluster machines, assuming they are already setup for InfiniBand+RDMA, require the following dependencies to be able to execute the binaries:

sudo apt install -y coreutils gawk python3 zip tmux gcc numactl libmemcached-dev memcached openjdk-8-jre-headless

The proper version of Mellanox OFED's InfiniBand drivers can be installed on the cluster machines via:

wget http://www.mellanox.com/downloads/ofed/MLNX_OFED-5.3-1.0.0.1/MLNX_OFED_LINUX-5.3-1.0.0.1-ubuntu20.04-x86_64.tgz
tar xf MLNX_OFED_LINUX-5.3-1.0.0.1-ubuntu20.04-x86_64.tgz
sudo ./mlnxofedinstall

Build Dependencies

To build the evaluation binaries, you need the dependencies below.

Note: You can build and package the binaries in a cluster machine, the gateway or another machine. It is important, however, that you build the binaries in a machine with the same distro/version as the cluster machines, otherwise the binaries may not work. For example, you can use a docker container to build and package the binaries. Alternatively, you can use one of the machines in the cluster.

Install the required dependencies on a vanilla Ubuntu 20.04 installation via:

sudo apt update
sudo apt -y install \
    python3 python3-pip \
    gawk build-essential cmake ninja-build \
    git libssl-dev \
    libmemcached-dev \
    libibverbs-dev # only if Mellanox OFED is not installed.
pip3 install --upgrade "conan>=1.63.0,<2.0.0"

Building and Deploying the Binaries

Assuming all the machines in your cluster have the same configuration, you need to:

• build all the necessary binaries, for example in a deployment machine,
• package them and deploy them on all 8 machines.

Recursively Cloning this Repository

First, clone this repository on the gateway, including the swarm-kv submodule, via:

git clone https://github.com/LPD-EPFL/swarm-artifacts.git --recurse-submodules
cd swarm-artifacts

Make sure your working path has no spaces or special characters (e.g. /home/user/mydir-1/swarm-artifacts is fine).

If you are not using our pre-configured cluster, set the proper FQDN of the cluster's machines in scripts/config.sh.

Building the Binaries

Build the evaluation binaries via:

./bin/swarm-kv/build.sh distclean buildclean clean # cleans potential leftovers
./bin/swarm-kv/build.sh swarm-kv fusee
./bin/swarm-kv/build.sh swarm-kv fusee # due to conan concurrency issues, the first command might run into missing dependencies 

Binaries for SWARM-KV and FUSEE will appear in bin/swarm-kv/swarm-kv/build/bin and bin/swarm-kv/fusee/build/bin, respectively.

Downloading YCSB

Download YCSB binaries via:

./download-ycsb.sh

This should download ycsb-0.12.0.tar.gz in the current directory, and print ycsb-0.12.0.tar.gz: OK if the checksum matches.

Deploying the Binaries

Zip the binaries and prepare their deployment via:

./bin/zip-binaries.sh
./prepare-deployment.sh # generates deployment.zip

Then, for each cluster server, you will need to:

• create a swarm-artifacts directory with the same path as the one on the gateway,
• send deployment.zip to said directory,
• unzip deployment.zip in said directory,
• untar swarm-artifacts/ycsb-0.12.0.tar.gz in said directory,
• rename the swarm-artifacts/ycsb-0.12.0 directory to swarm-artifacts/YCSB,
• unzip swarm-artifacts/bin/bin.zip in the swarm-artifacts/bin directory.

On our pre-configured cluster, this can be done from the gateway via a single:

./send-deployment.sh

If anything goes wrong, or you want to clean workers, you can undo the deployment via ./undo-deployment.sh. This will also delete remote logs.

Once the deployment is done, do not move the swarm-artifacts directory.

As a sanity check, the swarm-artifact directory of each worker should contain the bin, experiments, scripts, workloads and YCSB subdirectories.

Running Experiments

Experiments

Once the binaries are deployed, you can reproduce the results presented in our paper from the gateway by running the following scripts. During the kick-the-tires period, we invite you to run the scripts of figure 5 as a sanity check.

Note: Due to differences in hardware and software configuration, you can expect the pre-configured cluster we provide to achieve up to both 10% higher latency and 10% lower throughput than the setup used in the accepted version of the paper. However, such degradations should not affect the behaviors and relative comparisons presented in the paper.

Each experiment takes roughly takes ~15-30 minutes to run.

Figure 5

experiments/fig5-latency-cdf.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig5-latency-cdf.py # print the data points
plot-datapoints/fig5-latency-cdf.py # plot as pdf in output-plots/

Figure 6

experiments/fig6-tput-latency.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig6-tput-latency.py # print the data points
plot-datapoints/fig6-tput-latency.py # plot as pdf in output-plots/

Figure 7

experiments/fig7-scaling-clients.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig7-scaling-clients.py # print the data points
plot-datapoints/fig7-scaling-clients.py # plot as pdf in output-plots/

Figure 8

experiments/fig8-value-sizes.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig8-value-sizes.py # print the data points
plot-datapoints/fig8-value-sizes.py # plot as pdf in output-plots/

Figure 9

experiments/fig9-replication-factor.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig9-replication-factor.py # print the data points
plot-datapoints/fig9-replication-factor.py # plot as pdf in output-plots/

Figure 10

experiments/fig10-failure.sh # run a shorter experiment that focuses on the key points
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig10-failure.py # print the data points
plot-datapoints/fig10-failure.py # plot as pdf in output-plots/

Figure 11

experiments/fig11-single-key.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig11-single-key.py # print the data points
plot-datapoints/fig11-single-key.py # plot as pdf in output-plots/

Figure 12

experiments/fig12-metadata-buffers.sh # run the experiment
./gather-logs.sh # retrieve the logs from the workers to the gateway
print-datapoints/fig12-metadata-buffers.py # print the data points
plot-datapoints/fig12-metadata-buffers.py # plot as pdf in output-plots/

Navigating the code

How to navigate the code is described in the README of the swarm-kv repository.