Performance Optimization for All Flash Scale-Out Storage

Oh, Myoungwon; Eom, Jugwan; Yoon, Jungyeon; Yun, Jae Yeun; Kim, Seungmin; Yeom, Heon Y.

doi:10.1109/cluster.2016.11

Cited by 15 publications

(2 citation statements)

References 9 publications

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“…They also studied the impact of using high speed SSD drives for journaling. A similar study 24 proposed non‐blocking logging and lightweight transaction processing to boost the performance of Ceph with SSDs disks.…”

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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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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“…For instance, usage of snapshots could cause client nodes or MDSs to terminate unexpectedly. In addition, Ceph is designed with HDDs as its basis and needs improvements in performance when disks are replaced with SSDs, and data access pattern is random [71].…”

Section: Cephmentioning

confidence: 99%

Survey of Storage Systems for High-Performance Computing

Lüttgau

Kuhn

Duwe

et al. 2018

JSFI

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In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing.

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