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This repository contains the artifact for the Middleware'24 paper (to appear): "PvCC: A vCPU Scheduling Policy for DPDK-applied Systems at Multi-Tenant Edge Data Centers"

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PvCC: A vCPU Scheduling Policy for DPDK-applied Systems at Multi-Tenant Edge Data Centers

Table of Contents

Overview

This repository contains the artifact for the Middleware'24 paper (to appear).

Yuki Tsujimoto, Yuki Sato, Kenichi Yasukata, Kenta Ishiguro, and Kenji Kono. 2024. PvCC: A vCPU Scheduling Policy for DPDK-applied Systems at Multi-Tenant Edge Data Centers. In Proceedings of 25th International Middleware Conference (Middleware ’24)

This paper explores a practical means to employ Data Plane Development Kit (DPDK) in resource-limited multi-tenant edge data centers.

Getting Started

Prerequisites

  • Two Linux machines
    • We recommend Ubuntu 18.04 for the server and Ubuntu 20.04 for the client
  • Network access
  • SR-IOV capable 10G NIC
  • Software

Experimental Setup

We conduct the evaluation with the following machines (server and client).
CPU Intel Xeon Gold 5318Y CPU @ 2.10GHz
DRAM 128GiB, 4 * 32GiB DDR4 3200 MHz
NIC Intel X540-AT2 (direct connect)
Hyper Threading disabled
C-State disabled

The server machine runs Xen hypervisor v4.12.1 with Ubuntu 18.04 and Linux v4.15.0.

The client machine runs Ubuntu 20.04.

Server Side Setups

Build and Install PvCC

Clone this repository and fetch all submodules on the server machine.

$ git clone https://github.com/sslab-keio/PvCC
$ cd PvCC
$ git submodule update --init --recursive

Install build dependencies as follows.

$ sudo apt build-dep xen  # Need to enable source repository
$ sudo apt install build-essential bcc bin86 gawk bridge-utils iproute2 libcurl4-openssl-dev bzip2 module-init-tools transfig tgif texinfo texlive-latex-base texlive-latex-recommended texlive-fonts-extra texlive-fonts-recommended libpci-dev mercurial make gcc libc6-dev zlib1g-dev python python-dev python-twisted libncurses5-dev patch libvncserver-dev libsdl1.2-dev acpica-tools libbz2-dev e2fslibs-dev git uuid-dev ocaml ocaml-findlib libx11-dev bison flex xz-utils libyajl-dev gettext libpixman-1-dev libaio-dev markdown pandoc ninja-build meson libsystemd-dev python3 python3-dotenv

Build and install vanilla Xen and Xen equipped with PvCC.

$ cd $ARTIFACT_ROOT           # Path to the cloned repository
$ build-xen/vanilla/build.sh  # For vanilla Xen
$ build-xen/pvcc/build.sh     # For PvCC

Reboot into the vanilla Xen with the following commands.

$ echo "GRUB_DISABLE_SUBMENU=y" | sudo tee -a /etc/default/grub
$ echo 'GRUB_CMDLINE_LINUX_DEFAULT="nomdmonddf nomdmonisw intel_iommu=on pci=assign-busses"' | sudo tee -a /etc/default/grub
$ echo 'GRUB_CMDLINE_XEN="timer_slop=0 max_cstate=0"' | sudo tee -a /etc/default/grub
$ sudo update-grub
$ sudo grub-reboot "Ubuntu GNU/Linux, with Xen 4.12.1-ae-vanilla and Linux `uname -r`"
$ sudo reboot

Compiling vCPU scaling API server

The vCPU scaling API server scales up and down the number of dedicated physical CPUs for I/O.

Compile the vCPU scaling API server with the following commands.

$ cd ${ARTIFACT_ROOT}/pvcc-server
$ make

Network configuration

Netplan configuration

${ARTIFACT_ROOT}/netplan/server/50-ae.yaml is a netplan configuration for the server.

Update the following points according to your environment.

  • <netif SR-IOV PF>
    • an SR-IOV physical function
  • <netif external>
    • a network interface to the external network
network:
    ethernets:
        <netif SR-IOV PF>:
            virtual-function-count: 32
    bridges:
        xenbr0:
            addresses: [192.168.70.250/24]
            dhcp4: false
        xenbr1:
            interfaces:
              - <netif external>
            dhcp4: true
    version: 2

Apply the updated netplan configuration.

$ cd $ARTIFACT_ROOT
$ sudo cp netplan/server/50-ae.yaml /etc/netplan
$ sudo netplan apply

SSH setup

Set up an SSH key so that the root user can log in to the client machine.

Fill in the username and the client's address for SSH login in the .env file, copied from .env.template.

$ cd ${ARTIFACT_ROOT}
$ cp .env.template .env
$ $EDITOR .env

# .env file example:
CLIENT_USERNAME="somebody"
CLIENT_ADDR="client.machine.example.com"
## or
CLIENT_ADDR="192.168.50.10"

Preparing VM configuration files

A VM configuration file needs an absolute path to the kernel, initrd, and root filesystem image.

By default, the seastar and sysbench VMs need a Linux kernel v4.15.0 image and initrd under /boot. The F-Stack VMs need a Linux kernel v5.15.0 image and initrd. Update kernel and ramdisk parameters within configuration files according to your environment.

The script domu-configs/populate_path.sh completes a placeholder of the root filesystem image path inside the configuration file.

$ cd $ARTIFACT_ROOT
$ domu-configs/populate_path.sh

Preparing VM root filesystem images

Retrieve base images by debootstrap command.

$ sudo apt install debootstrap
$ cd $ARTIFACT_ROOT
$ sudo debootstrap bionic images/debootstrap-bionic
$ sudo debootstrap jammy images/debootstrap-jammy

Run the following commands to create each image. Before executing the following scripts, place an SSH key to log in to VMs under /root/.ssh/. These scripts register /root/.ssh/*.pub to VMs' authorized_keys.

$ cd $ARTIFACT_ROOT
$ images/seastar.sh   # For seastar
$ images/fstack.sh    # For F-Stack
$ images/sysbench.sh  # For sysbench

Client Side Setups

Installing benchmark tools

Install mutilate, wrk2, and memtier_benchmark on the client machine.

# Install build dependencies
$ sudo apt install -y scons libevent-dev gengetopt libzmq3-dev make libevent-dev libpcre3-dev libssl-dev autoconf automake pkg-config make g++

$ cd $HOME
$ git clone https://github.com/leverich/mutilate
$ cd mutilate
$ git checkout d65c6ef7c2f78ae05a9db3e37d7f6ddff1c0af64
$ sed -i -E 's/print "(.*)"/print("\1")/g' SConstruct
$ scons

$ cd $HOME
$ git clone https://github.com/giltene/wrk2
$ cd wrk2
$ git checkout 44a94c17d8e6a0bac8559b53da76848e430cb7a7
$ make

$ cd $HOME
$ git clone https://github.com/RedisLabs/memtier_benchmark
$ cd memtier_benchmark
$ git checkout 0c0c440a0cb8f7e0195b86520cd261f823f8e2e0
$ autoreconf -ivf
$ ./configure
$ make

Network configuration

A netplan configuration file for the server (${ARTIFACT_ROOT}/netplan/client/50-ae.yaml) is as follows.

Update the following point according to your environment.

  • <netif to server>
    • a network interface to communicate with the server machine
network:
  ethernets:
    <netif to server>:
      dhcp4: no
      addresses: [10.0.0.250/24]

Apply the updated configuration.

$ sudo cp <path/to/the/config/above> /etc/netplan
$ sudo netplan apply

Major Claims

  • Claim 1: DPDK applied system can utilize CPU cycles more efficiently than the kernel network stack. (Figure 2)
  • Claim 2: In consolidated setup, serving latency of DPDK-applied systems increases. (Figure 6)
  • Claim 3: The serving latency can be decreased by adopting a microsecond-scale time slice. (Figure 6)
  • Claim 4: Short time slices trade off maximum throughput for low serving latency. (Figure 10)
  • Claim 5: This trade-off can be mitigated with vCPU scaling API by changing the CPU overcommitment ratio. (Figure 11)

Experiment workflows

Experiment for Claim 1

This experiment shows that DPDK applied system can utilize CPU cycles more efficiently than the kernel network stack.

To conduct the experiment, run:

$ cd $ARTIFACT_ROOT

$ sudo python3 experiments/fig02.py --threads 1 12 24 --trial 1

# Draw a figure
$ sudo docker compose run --rm graph python3 /scripts/fig02.py --threads 1 12 24 --trial 1

The server runs the Seastar, a high performance TCP/IP stack with two networking modes, kernel TCP/IP stack and DPDK network stack.

The client runs mutilate to perform GET 100% load while changing the concurrency of TCP connections.

This experiment compares the achieved throughput of kernel TCP/IP stack and DPDK.

You will see that DPDK achieves around higher throughput than the kernel network stack, using the same amount of CPU resources (Claim 1).

Evaluation with full data 
$ cd $ARTIFACT_ROOT

$ sudo python3 experiments/fig02.py
$ ls data/fig02/dpdk data/fig02/linux

$ sudo docker compose run --rm graph python3 /scripts/fig02.py
$ ls figures/fig02.pdf

Experiment for Claim 2,3

This experiment compares PvCC with the Xen's default schedulers, the Credit and Credit2 scheduler.

Then, run the following commands.

$ cd $ARTIFACT_ROOT

# Run with the vanilla Xen setup
$ sudo python3 experiments/fig06_vanilla.py --workloads memcached --rates 5000 40000 90000 --trial 1
# --workloads [WORKLOADS, ...]  # Execute specific workload only
# --rates [RATES, ...]          # Restrict data with the specific RATES
# --trial TRIAL

# Reboot into Xen equipped with PvCC.
$ sudo grub-reboot "Ubuntu GNU/Linux, with Xen 4.12.1-ae-pvcc and Linux `uname -r`"
$ sudo reboot

# Apply netplan again
$ sudo netplan apply

# Run with the PvCC setup
$ sudo python3 experiments/fig06_pvcc.py --workloads memcached --rates 5000 40000 90000 --trial 1

# Draw a figure
$ sudo docker compose run --rm graph python3 /scripts/fig06.py --workloads memcached --rates 5000 40000 90000 --trial 1

The server runs four single-vCPU Seastar memcached instances on one physical core. The client runs mutilate to generate load for only one instance.

This experiment reports resulting throughput and 99th-percentile latency.

In figures/fig06.pdf, you will see that PvCC achieves 99th-percentile latency of sub-milliseconds while Credit or Credit2 scheduler always causes a 99th-percentile latency of several milliseconds.

For throughput, PvCC will be saturated earlier than Credit and Credit2.

This result shows that the serving latency of DPDK-applied systems increases (Claim 2). It also shows that this overhead can be mitigated by adopting microsecond-scale timeslice.

Evaluation with full data 
# Reboot into Xen equipped with PvCC.
$ sudo grub-reboot "Ubuntu GNU/Linux, with Xen 4.12.1-ae-pvcc and Linux `uname -r`"
$ sudo reboot

# Apply netplan again
$ sudo netplan apply

# Run the experiment
$ cd $ARTIFACT_ROOT
$ sudo python3 experiments/fig06_pvcc.py
$ ls data/fig06/memcached/pvcc
$ ls data/fig06/nginx/pvcc
$ ls data/fig06/redis/pvcc

# Draw a figure
$ cd $ARTIFACT_ROOT
$ sudo docker compose run --rm graph python3 /scripts/fig06.py
$ ls figures/fig06.pdf

Experiment for Claim 4

This experiment shows how the short time slices trade-off maximum throughput for low serving latency.

Run the following commands to conduct the experiment.

$ cd $ARTIFACT_ROOT
$ sudo python3 experiments/fig10.py --vms 4 --tslices 100 1000 --rates 1000 40000 100000 --trial 1

# Draw a figure
$ sudo docker compose run --rm graph python3 /scripts/fig10.py --vms 4 --tslices 100 1000 --rates 1000 40000 100000 --trial 1

The server runs four single-vCPU Seastar memcached instances with 100µs or 1000µs timeslice.

The client runs mutilate while changing the offering load.

This experiment reports resulting throughput and 99th-percentile latency.

In figures/fig10.pdf, you will see that short time slices offer the latency advantage when the incoming request rate is low. On the other hand, instances with the short timeslices (100µs) are saturated earlier than the longer timeslice (1000µs) one.

This means that short time slices offer low latency at the expense of maximum throughput (Claim 4).

Evaluation with restrictive data 
$ cd $ARTIFACT_ROOT
$ sudo python3 experiments/fig10.py
$ ls data/fig10
$ sudo docker compose run --rm graph python3 /scripts/fig10.py
$ ls figures/fig10.pdf

Experiment for Claim 5

This experiment shows how the vCPU scaling API contributes to handling load increase.

Run the following commands.

$ cd $ARTIFACT_ROOT
$ sudo python3 experiments/fig11.py
$ ls data/fig11
$ sudo docker compose run --rm graph python3 /scripts/fig11a.py
$ sudo docker compose run --rm graph python3 /scripts/fig11c.py
$ ls figures/fig11a.pdf figures/fig11c.pdf

The server runs four single-vCPU Seastar memcached instances (Latency-Sensitive tasks) and one 24-vCPU Sysbench instance (Best-Effort task).

All tasks runs on 6 physical cores.

The client runs mutilate to generate dynamically changing load across four memcached instances.

This experiment evaluates the effectiveness of the vCPU scaling API compared to two other approaches, "Naive" and "Pin".

  • Naive: All tasks share 6-core resources.
  • Pin: Dedicate one physical core to each latency-sensitive task

This experiment reports the aggregated throughput of Seastar memcached and Sysbench.

In figures/fig11a.pdf, you will see that the PvCC maintains high throughput of all time.

In figures/fig11c.pdf, you will observe that, with the vCPU scaling API, the best-effort task can utilize the CPU time while the load for latency-sensitive tasks is low.

These results show that the client can maintain high throughput of latency-sensitive tasks by the vCPU scaling API(Claim 5).

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This repository contains the artifact for the Middleware'24 paper (to appear): "PvCC: A vCPU Scheduling Policy for DPDK-applied Systems at Multi-Tenant Edge Data Centers"

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