DSS

Disaggregated Storage Solution

What is DSS

DSS is a rack-scalable, very high read-bandwidth-optimized, Amazon S3-compatible object storage solution developed by Samsung. It utilizes a disaggregated architecture, enabling independent scaling of storage and compute. It features an end-to-end KV semantic communication stack, entirely eliminating the legacy software storage stack. All storage communication uses the NVMeOf-KV-RDMA protocol introduced and open sourced by Samsung. With zero-copy transfer, it achieves high end-to-end performance. The DSS client-side stack includes a high performance wrapper library for simple application integration. Applications utilizing the DSS client library eliminate the need for bucket semantics, key distribution and load balancing between server-side S3 endpoints.

How to build, deploy, and use DSS software

DSS Performance

S3 Benchmark results:

Results are aggregated with the following specification:

• 4-node Dell R7525 Cluster
  • 16x Samsung PM1733 3.84TB NVMe drives per Node
  • 4x Dual-port 100g Mellanox CX-5 NIC per node
  • Dual-socket AMD EPYC 7742 64-Core Processors
  • 1TB DIMM per Node

Build DSS - Docker

DSS is optimally built via Docker using the scripts documented below.

Build All - Docker

Build all of the DSS artifacts and its dependency artifacts using one script:

./scripts/docker/build_all.sh

Build Dependencies - Docker

Optionally, build only the dependencies artifacts:

./scripts/docker/build_gcc.sh
./scripts/docker/build_aws-sdk.sh
./scripts/docker/build_kernel.sh
./scripts/docker/build_mlnx-tools.sh

Build DSS Artifacts - Docker

Optionally, build only the DSS artifacts:

./scripts/docker/build_dss-sdk.sh
./scripts/docker/build_minio.sh
./scripts/docker/build_dss-client.sh
./scripts/docker/build_datamover.sh

Build DSS

Alternatively, DSS can be built natively, but all dependencies must be installed first.

Prerequisites

Operating system requirements

DSS build and runtime is presently supported on CentOS 7.8.

Build package dependencies

Install the following packages / modules to build DSS and its external dependencies:

sudo yum install epel-release centos-release-scl-rh -y
sudo yum install bc bison boost-devel cmake cmake3 CUnit-devel devtoolset-11 dpkg elfutils-libelf-devel \
  flex gcc gcc-c++ git glibc-devel gmp-devel jemalloc-devel Judy-devel libaio-devel libcurl-devel libmpc-devel \
  libuuid-devel make man-db meson mpfr-devel ncurses-devel numactl-devel openssl openssl-devel patch \
  pulseaudio-libs-devel python3 python3-devel python3-pip rdma-core-devel redhat-lsb-core rpm-build \
  snappy-devel tbb-devel wget zlib-devel -y
sudo python3 -m pip install pybind11 gcovr==5.0

NOTE: User-built GCC and AWS-SDK-CPP RPMs must be installed on the build machine.

On initial build:

1. Build GCC: ./scripts/build_gcc.sh
2. Install the resulting GCC RPM: sudo yum install ./dss-ansible/artifacts/dss-gcc510*.rpm -y
3. Build AWS-SDK-CPP: ./scripts/build_aws-sdk.sh
4. Install the resulting AWS-SDK-CPP RPM: sudo yum install ./dss-ansible/artifacts/aws-sdk-cpp-*.rpm -y
5. Run the build_all.sh script: ./scripts/build_all.sh

Once the GCC and AWS RPMs are installed, only the build_all.sh script needs to be run on subsequent builds.

Dependency artifacts for GCC, kernel, aws-sdk-cpp, and mlnx-tools are staged under rpmbuilder and workspace directories of your home directory by default. By leaving them in-place, re-build of these upstream components will be skipped on subsequent builds.

Optional: Build individual components

DSS Dependency build scripts:

• Build GCC: ./scripts/build_gcc.sh
• Build aws-sdk-cpp: ./scripts/build_aws-sdk.sh
• Build kernel: ./scripts/build_kernel.sh
• Build mlnx-tools: ./scripts/build_mlnx-tools.sh

DSS individual components:

• Build dss-sdk: ./scripts/build_dss-sdk.sh
• Build dss-minio: ./scripts/build_minio.sh
• Build dss-client: ./scripts/build_dss-client.sh
• Build dss-datamover: ./scripts/build_datamover.sh

Deploy DSS

See dss-ansible README

Blogs and Papers

DSS: High I/O Bandwidth Disaggregated Object Storage System for AI Applications

High-Capacity SSDs for AI/ML using Disaggregated Storage Solution: Performance Test Results Show Promise