Ceph Distributed File System Benchmarks on an Openstack Cloud

Zhang, X.; Gaddam, S.; Chronopoulos, Anthony T.

doi:10.1109/ccem.2015.12

Cited by 14 publications

(10 citation statements)

References 4 publications

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“…Zhang et al [16] virtually deployed a Ceph cluster in an OpenStack environment to evaluate the performance of Ceph deployed in the virtual environment. They benchmarked the cluster's network performance using netcat and iperf.…”

Section: Related Workmentioning

confidence: 99%

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Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment

et al. 2021

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Data are important and ever growing in data-intensive scientific environments. Such research data growth requires data storage systems that play pivotal roles in data management and analysis for scientific discoveries. Redundant Array of Independent Disks (RAID), a well-known storage technology combining multiple disks into a single large logical volume, has been widely used for the purpose of data redundancy and performance improvement. However, this requires RAID-capable hardware or software to build up a RAID-enabled disk array. In addition, it is difficult to scale up the RAID-based storage. In order to mitigate such a problem, many distributed file systems have been developed and are being actively used in various environments, especially in data-intensive computing facilities, where a tremendous amount of data have to be handled. In this study, we investigated and benchmarked various distributed file systems, such as Ceph, GlusterFS, Lustre and EOS for data-intensive environments. In our experiment, we configured the distributed file systems under a Reliable Array of Independent Nodes (RAIN) structure and a Filesystem in Userspace (FUSE) environment. Our results identify the characteristics of each file system that affect the read and write performance depending on the features of data, which have to be considered in data-intensive computing environments.

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Section: Related Workmentioning

confidence: 99%

“…We can find various studies [15][16][17] which have measured the performance of file systems using various tools. Other papers [19,20] also describe their own tools to benchmark the file system.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment

et al. 2021

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“…Ceph storage is perfectly integrated into OpenStack. 50 As a result, four pools (i.e., logical partitions for storage) have been created, taking advantage of the capacity and performance provided by the different types of disks. Currently, the system has the following pools:…”

Section: Openstack Massive Storage Deployment With Cephmentioning

confidence: 99%

Toward a Spanish SKA Regional Centre fully engaged with open science

Garrido

Darriba

Sánchez–Expósito

et al. 2021

J. Astron. Telesc. Instrum. Syst.

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“…Zhang et al 21 investigated the performance of Ceph by using multiple storage benchmarking tools including Bonnie++ , dd and rados bench . The problem with this study it that Ceph is deployed on an Openstack cloud, and the additional level of virtualization can cause an extra overhead.…”

Section: Litterature Reviewmentioning

confidence: 99%

Performance analysis of distributed storage clusters based on kernel and userspace traces

Daoud

Dagenais

2020

Softw Pract Exp

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Distributed storage systems are commonly used in modern computing. They are highly scalable and offer data replication and fault tolerance. The complexity of those systems makes them difficult to debug using traditional tools. The existing tools are able to evaluate the overall performance of such systems but they do not provide enough information to find the root cause of performance issues. In this article, we propose a tracing-based performance analysis framework for storage clusters. We use a tracing strategy that reduces the tracing overhead in production systems. The traces collected from the different storage nodes are correlated and used to generate a data model that represents the cluster. Userspace tracing is used to gather data from the storage daemons, while Kernel tracing is used to provide detailed information about operating system internals such as disk queues, network queues and process scheduling. Efficient data structures are used to store the model and to generate metrics and graphical views. Our tool is used in different real world scenarios and is able to investigate interesting performance problems including I/O latencies, data replication and storage nodes failures.

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Ceph Distributed File System Benchmarks on an Openstack Cloud

Cited by 14 publications

References 4 publications

Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment

Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment

Toward a Spanish SKA Regional Centre fully engaged with open science

Performance analysis of distributed storage clusters based on kernel and userspace traces

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