COSBench: A Benchmark Tool for Cloud Object Storage Services

Zheng, Qing; Chen, Haopeng; Wang, Yaguang; Duan, Jiangang; Huang, Zhiteng

doi:10.1109/cloud.2012.52

Cited by 33 publications

(10 citation statements)

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“…In our experiments, the workloads are generated using COSBench [16], which upload, download and delete objects from 64 containers ("bucket" in the Amazon S3 terminology) in the Swift cluster. It is the future work to consider the dynamic workloads and different SLA definitions.…”

Section: Workloadsmentioning

confidence: 99%

Building Cost-Effective Storage Clouds

Zhang

Kant²

2014

2014 IEEE International Conference on Cloud Engineering

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Many application classes, such as archiving, backup of thousands of nodes in an organization, video sharing, etc., require highly reliable and scalable storage systems. Since it is now feasible to build such storage systems with advanced open source technologies, the challenge becomes how to best utilize those technologies to build and operate such a storage system with optimized cost and performance. The focus of this work is to provide an effective solution and key insights for this challenge within the context of the OpenStack Object Storage (Swift) platform.

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Section: Workloadsmentioning

confidence: 99%

Building Cost-Effective Storage Clouds

Zhang

Kant²

2014

2014 IEEE International Conference on Cloud Engineering

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“…In [13], the authors develop a tool to assess the performance of services directed at object storage devices. It was stated that the following metrics should be considered in order to properly evaluate a solution: mean response time (elapsed time between the beginning and the completion of an operation), throughput (total number of transactions per second) and bandwidth (total amount of data transferred per second).…”

Section: IIImentioning

confidence: 99%

“…In [14], it is stated that the three major metrics that should be taken into account by organizations wanting to take advantage of cloud storage are: performance, availability, and scalability. It argues that an architecture aimed at cloud storage must offer "unlimited" storage capacity, as well as lay the foundation for the complete elimination of the need for daily backups through cloud data replication mechanisms Both [13] and [14] point out that to analyze a storage structure in a private cloud, one should measure the response time, throughput and bandwidth using the following variables: concurrency (multiple concurrent connections), file size (from very small to very large) and the type of workload (reading, writing and mixed).…”

Section: IIImentioning

confidence: 99%

Comparison of Performance, Availability and Scalability of a Private Cloud Storage Infrastructure Service for Document Preservation

Araújo

Ferreira

Drummond

2017

JADI

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Cloud computing platforms provide easy and transparent access to a company’s storage infrastructure through services. it allows users to store their data at remote disks and access them anytime from any place. On the other hand, with the deployment of Electronic Document Management (EDM) to manage the insertion, sharing, and retrieval of information assigned to a digital document it is necessary that the processes of searching, indexing and storage of digital documents are carried out efficiently. Besides the use of computational tools focused on archiving digital documents, the storage solution must provide reliability and authenticity on the availability of digital documents. With object storage systems being increasingly accepted as replacements for traditional file or block systems and recognized as a preferred way to expose one’s storage infrastructure, it is important to effectively measure the performance of these services. This paper addresses the deployment project of a content-addressable storage infrastructure in a large company and conducts performance, availability and scalability evaluation to measure the gains in r/w access of the proposed solution in different scenarios proposed.

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“…One prototype version of our tool was presented in a previous short report [32]. Compared with this release, our current version has been fully revised with a stronger system architecture which gives birth to the high extensibility and scalability of the tool.…”

Section: Related Workmentioning

confidence: 99%

COSBench

Zheng

Chen

Wang³

et al. 2013

Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

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With object storage systems being increasingly recognized as a preferred way to expose one's storage infrastructure to the web, the past few years have witnessed an explosion in the acceptance of these systems. Unfortunately, the proliferation of available solutions and the complexity of each individual one, coupled with a lack of dedicated workload, makes it very challenging for one to evaluate and tune the performance of different systems. To help address this problem, we present the Cloud Object Storage Benchmark (COSBench). It is a benchmark tool that we have developed at Intel with the goal of facilitating both performance comparison and system optimization of these systems. In this paper, we describe the design and implementation of this tool, focusing on its extensibility and scalability. In addition, we discuss how people can use this tool to perform system characterization and how the latter can facilitate system comparison and optimization. To demonstrate the value of our tool, we report the results of our experiments conducted on two Swift setups we built in our lab. We also share some of our experiences in turning our setups to achieve higher performance.

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COSBench: A Benchmark Tool for Cloud Object Storage Services

Cited by 33 publications

References 1 publication

Building Cost-Effective Storage Clouds

Building Cost-Effective Storage Clouds

Comparison of Performance, Availability and Scalability of a Private Cloud Storage Infrastructure Service for Document Preservation

COSBench

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