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Pinpointing and scheduling access conflicts to improve internal resource utilization in solid-state drives

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Abstract

Modern solid-state drives (SSDs) are integrating more internal resources to achieve higher capacity. Parallelizing accesses across internal resources can potentially enhance the performance of SSDs. However, exploiting parallelism inside SSDs is challenging owing to real-time access conflicts. In this paper, we propose a highly parallelizable I/O scheduler (PIOS) to improve internal resource utilization in SSDs from the perspective of I/O scheduling. Specifically, we first pinpoint the conflicting flash requests with precision during the address translation in the Flash Translation Layer (FTL). Then, we introduce conflict eliminated requests (CERs) to reorganize the I/O requests in the device-level queue by dispatching conflicting flash requests to different CERs. Owing to the significant performance discrepancy between flash read and write operations, PIOS employs differentiated scheduling schemes for read and write CER queues to always allocate internal resources to the conflicting CERs that are more valuable. The small dominant size prioritized scheduling policy for the write queue significantly decreases the average write latency. The high parallelism density prioritized scheduling policy for the read queue better utilizes resources by exploiting internal parallelism aggressively. Our evaluation results show that the parallelizable I/O scheduler (PIOS) can accomplish better SSD performance than existing I/O schedulers implemented in both SSD devices and operating systems.

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Acknowledgements

This work was supported in part by the Advanced Research Project of China (31511010202) and the National Key Research and Development Program of China (2016YFB0200203).

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Xuchao Xie, Liquan Xiao, Dengping Wei, Qiong Li & Zhenlong Song

  2. Department of Computer Science, University of Minnesota, Minneapolis, MN, 55455, USA

    Xiongzi Ge

Authors
  1. Xuchao Xie
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  2. Liquan Xiao
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  3. Dengping Wei
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  4. Qiong Li
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  5. Zhenlong Song
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  6. Xiongzi Ge
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Corresponding author

Correspondence to Liquan Xiao.

Additional information

Xuchao Xie is currently a PhD candidate in the College of Computer at the National University of Defense Technology, China. His current research interests include high-performance computing, file and storage systems with solid-state drives, and shingled magnetic recording drives.

Liquan Xiao received his MS and PhD degrees in computer science from the National University of Defense Technology (NUDT), China. Currently he is a professor at NUDT. His research interests include architectures of high-performance computing, high-speed interconnected networks, system integration, and power management.

Dengping Wei received her MS and PhD degrees in computer science from the National University of Defense Technology (NUDT), China in 2005 and 2011, respectively. Currently she is an assistant professor in the College of Computer, NUDT. Her research interests mainly involve high-performance computing, storage technology, and big data.

Qiong Li is currently a professor in College of Computer, National University of Defense Technology (NUDT), China. She received her MS and PhD in computer science from NUDT. Her current research interests include high performance computing and storage technology.

Zhenlong Song is an associate professor in the College of Computer at the National University of Defense Technology (NUDT), China. He received his PhD degree from the NUDT. His current research interests include high-performance computing and storage technology.

Xiongzi Ge received his PhD degree in computer science from the University of Minnesota, USA in 2017, and a PhD in computer architecture from Huazhong University of Science and Technology, China in 2012. Since 2016, he has been a fulltime researcher in the Advanced Technology Group at NetApp, Inc. His major research interests include big data processing, cloud computing, and data storage systems.

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Xie, X., Xiao, L., Wei, D. et al. Pinpointing and scheduling access conflicts to improve internal resource utilization in solid-state drives. Front. Comput. Sci. 13, 35–50 (2019). https://doi.org/10.1007/s11704-018-7113-1

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  • DOI: https://doi.org/10.1007/s11704-018-7113-1

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