Performance and Sizing Guide: Red Hat Ceph Storage on QCT Server

Performance and Sizing Guide: Red Hat Ceph Storage on QCT Server

Performance and Sizing Guide: Red Hat Ceph Storage on QCT Servers

Executive Summary

Ceph users frequently request simple, optimized cluster configurations for different workload types. Common requests are for throughput-optimized and capacity-optimized workloads, but IOPS-intensive workloads on Ceph are also emerging. To address the need for performance, capacity, and sizing guidance, Red Hat and QCT (Quanta Cloud Technology) have performed extensive testing to characterize optimized configurations for deploying Red Hat Ceph Storage on a range of QCT servers.

Document Purpose

The purpose of this document is to characterize and compare the performance of Red Hat® Ceph Storage on various QCT (Quanta Cloud Technology) servers. Optimal Ceph cluster configurations are identified for general workload categories. As a reference architecture, this document provides details on cluster hardware, software, and network configuration combined with performanceresults. The testing methodology is also provided, and is based on the standardized Ceph Benchmarking Tool, available in a GitHub repository under the Ceph organization. The study described herein largely used off-the-shelf hardware and software components, and did not make a detailed study of  changing various configuration settings within the kernel, Ceph, XFS®, or the network.

Introduction

As the need for storage escalates, enterprises of all kinds are seeking to emulate efficiencies achieved by public cloud providers—with their highly successful software-defined cloud data centermodels based on standard servers and open source software. At the same time, the $35 billion storage market is undergoing a fundamental structural shift, with storage capacity returning to the server following decades of external NAS and SAN growth.2 Software-defined scale-out storage has emerged as a viable alternative, where standard servers and independent software unite to provide data access and highly available services across the enterprise.
The combination of QCT servers and Red Hat Storage software squarely addresses these industry trends, and both are already at the heart of many public cloud datacenters. QCT is reinventing datacenter server technology to boost storage capacity and density, and redesigning scalable hardware for cloud applications. As the world’s largest enterprise software company with an open source development model, Red Hat has partnered with several public cloud providers to provide Ceph and Gluster storage software in production environments. Together, QCT servers and Red Hat Ceph Storage provide software-defined storage solutions for both private and public clouds, helping to accelerate the shift away from costly, proprietary external storage solutions. Red Hat Ceph Storage significantly lowers the cost of storing enterprise data and helps enterprises manage exponential data growth. The software is a robust, petabyte-scale storage platform for enterprises deploying public or private clouds. As a modern storage system for cloud deployments,Red Hat Ceph Storage offers mature interfaces for enterprise block and object storage, making it well suited for archival, rich media, and cloud infrastructure workloads like OpenStack®. Delivered in a unified self-healing and self-managing platform with no single point of failure, Red Hat Ceph Storage handles data management so businesses can focus on improving application availability. Running Red Hat Ceph Storage on QCT servers provides open interaction with a community-based software development model, backed by the 24x7 support of the world’s most experienced open source software company. Use of standard hardware components helps ensure low costs, while QCT’s innovative development model lets organizations iterate more rapidly on a family of server designs optimized for different types of Ceph workloads. Unlike scale-up storage solutions, Red Hat Ceph Storage on QCT servers lets organizations scale out to thousands of nodes, with the ability to scale storage performance and capacity independently, depending on the needs of the application and the chosen storage server platform.


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