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AutoSSD: an Autonomic SSD Architecture

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dc.contributor.authorKim, Bryan S.-
dc.contributor.authorYang, Hyun Suk-
dc.contributor.authorMin, Sang Lyul-
dc.date.accessioned2022-05-24T06:40:27Z-
dc.date.available2022-05-24T06:40:27Z-
dc.date.created2022-05-24-
dc.date.issued2018-
dc.identifier.urihttps://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/28101-
dc.description.abstractFrom small mobile devices to large-scale storage arrays, flash memory-based storage systems have gained a lot of popularity in recent years. However, the uncoordinated use of resources by competing tasks in the flash translation layer (FTL) makes it difficult to guarantee predictable performance. In this paper, we present AutoSSD, an autonomic SSD architecture that self-manages FTL tasks to maintain a high-level of QoS performance. In AutoSSD, each FTL task is given an illusion of a dedicated flash memory sub-system, allowing tasks to be implemented oblivious to others and making it easy to integrate new tasks to handle future flash memory quirks. Furthermore, each task is allocated a share that represents its relative importance, and its utilization is enforced by a simple and effective scheduling scheme that limits the number of outstanding flash memory requests for each task. The shares are dynamically adjusted through feedback control by monitoring key system states and reacting to their changes to coordinate the progress of FTL tasks. We demonstrate the effectiveness of AutoSSD by holistically considering multiple facets of SSD internal management, and by evaluating it across diverse workloads. Compared to state-of-the-art techniques, our design reduces the average response time by up to 18.0%, the 3 nines (99.9%) QoS by up to 67.2%, and the 6 nines (99.9999%) QoS by up to 76.6% for QoS-sensitive small reads.-
dc.language영어-
dc.language.isoen-
dc.publisherUSENIX ASSOC-
dc.subjectFLASH TRANSLATION LAYER-
dc.titleAutoSSD: an Autonomic SSD Architecture-
dc.typeArticle-
dc.contributor.affiliatedAuthorYang, Hyun Suk-
dc.identifier.wosid000508006700052-
dc.identifier.bibliographicCitationPROCEEDINGS OF THE 2018 USENIX ANNUAL TECHNICAL CONFERENCE, pp.677 - 689-
dc.relation.isPartOfPROCEEDINGS OF THE 2018 USENIX ANNUAL TECHNICAL CONFERENCE-
dc.citation.titlePROCEEDINGS OF THE 2018 USENIX ANNUAL TECHNICAL CONFERENCE-
dc.citation.startPage677-
dc.citation.endPage689-
dc.type.rimsART-
dc.type.docTypeProceedings Paper-
dc.description.journalClass3-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.subject.keywordPlusFLASH TRANSLATION LAYER-
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