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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Realistic and Scalable Benchmarking Cloud File Systems: Practices and Lessons from AliCloud
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Realistic and Scalable Benchmarking Cloud File Systems: Practices and Lessons from AliCloud

机译：现实且可扩展的基准化云文件系统：AliCloud的实践和教训

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摘要

The past decade has witnessed the rapid boom of cloud computing. Many public cloud infrastructures have been implemented and serve millions of tenants. Cloud file systems, which take charge of petabyte-scale data storage, play a crucial role in the performance of cloud infrastructures. Typical cloud file systems, including GFS, HDFS and Ceph, have attracted notable research efforts for performance evaluation and optimization. However, due to the heterogeneity and complexity of I/O workload characteristics in cloud environments, it is still challenging to conduct an accurate and efficient performance evaluation. To address this problem, we collected a two-week I/O workload trace from a 2,500-node production cluster in AliCloud, which is one of the largest cloud providers in Asia. Using the AliCloud trace, we characterized the I/O workload and data distribution, and compared two cloud services in multiple perspectives, including the request arrival pattern, request size, data population and so on. A list of observations and implications were derived and applied to help design a cloud file system benchmarking suite, called Porcupine. Porcupine aims to deploy a scalable and efficient performance evaluation on cloud file systems using realistic I/O workloads. We conducted a group of validation experiments, which demonstrated that Porcupine can achieve high accuracy and scalability. This paper provides our experiences and lessons in generating I/O workloads and deploying performance tests on cloud file systems, which we believe will be insightful to the cloud computing community in general.
机译：过去十年见证了云计算的飞速发展。已经实现了许多公共云基础架构，并为数百万个租户提供服务。负责PB级数据存储的云文件系统在云基础架构的性能中起着至关重要的作用。包括GFS，HDFS和Ceph在内的典型云文件系统吸引了众多有关性能评估和优化的研究工作。但是，由于云环境中I / O工作负载特征的多样性和复杂性，进行准确而有效的性能评估仍然具有挑战性。为了解决这个问题，我们从AliCloud（一个亚洲最大的云提供商之一）中的2500个节点的生产集群中收集了为期两周的I / O工作负载跟踪。使用AliCloud跟踪，我们对I / O工作负载和数据分布进行了表征，并从多个角度比较了两个云服务，包括请求到达模式，请求大小，数据填充等。得出了一系列观察结果和含义，并将其应用于帮助设计称为Porcupine的云文件系统基准测试套件。 Porcupine旨在使用实际的I / O工作负载在云文件系统上部署可扩展且高效的性能评估。我们进行了一组验证实验，证明了豪猪可以实现高精度和可扩展性。本文提供了我们在生成I / O工作负载和在云文件系统上部署性能测试的经验和教训，我们相信这些知识和经验将对整个云计算社区产生深刻的影响。

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