Command queue-aware host I/O stack for mobile flash storage

Han, Kyuhwa; Shin, Dongkun

doi:10.1016/j.sysarc.2020.101758

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…The I/O latencies of the GALRU were 9.8%, 5.3%, 2.7%, and 2.1% lower than those of the LRU when the numbers of outstanding I/O requests were 4, 8, 16, and 32, respectively. Considering that the intensity of the workload on systems [47], [48] is kept low in practice for a considerable amount of time, the GALRU is sufficiently effective in improving performance.…”

Section: ) Impact Of Workload Intensity On I/o Latencymentioning

confidence: 99%

GALRU: A Group-Aware Buffer Management Scheme for Flash Storage Systems

Kwak

Lee

et al. 2020

IEEE Access

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Many flash storage systems divide input/output (I/O) requests that require large amounts of data into sub-requests to exploit their internal parallelism. In this case, an I/O request can be completed only after all sub-requests have been completed. Thus, non-critical sub-requests that are completed quickly do not affect I/O latency. To efficiently reduce I/O latency, we propose a buffer management scheme that allocates buffer space by considering the relationship between the processing time of the sub-request and I/O latency. The proposed scheme prevents non-critical sub-requests from wasting ready-to-use buffer space by avoiding the situation in which buffer spaces that are and are not ready to use are allocated to an I/O request. To allocate the same type of buffer space to an I/O request, the proposed scheme first groups sub-requests derived from the same I/O request and then applies a policy for allocating buffer space in units of subrequest groups. When the ready-to-use buffer space is insufficient to be allocated to the sub-request group being processed at a given time, the proposed scheme does not allocate it to the sub-request group but it instead sets it aside for future I/O requests. The results of the experiments to test the proposed scheme show that it can reduce I/O latency by up to 24% compared with prevalent buffer management schemes.

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Section: ) Impact Of Workload Intensity On I/o Latencymentioning

confidence: 99%