Reinforcement Learning-Assisted Cache Cleaning to Mitigate Long-Tail Latency in DM-SMR
DM-SMR adopts Persistent Cache (PC) to accommodate non-sequential write operations. However, the PC cleaning process induces severe long-tail latency. In this paper, we propose to mitigate the tail latency of PC cleaning by using Reinforcement Learning (RL). Specifically, a real-time lightweight Q-l...
Saved in:
| Published in: | 2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 103 - 108 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
05.12.2021
|
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | DM-SMR adopts Persistent Cache (PC) to accommodate non-sequential write operations. However, the PC cleaning process induces severe long-tail latency. In this paper, we propose to mitigate the tail latency of PC cleaning by using Reinforcement Learning (RL). Specifically, a real-time lightweight Q-learning model is built to analyze the idle window of I/O workloads, based on which PC cleaning is judiciously scheduled, thereby maximally utilizing the I/O idle window and effectively hiding the tail latency from regular requests. We implement our technique inside a Linux device driver with an emulated SMR drive. Experimental results show that our technique can reduce the tail latency by 57.65% at 99.9th percentile and the average response time by 46.11% compared to a typical SMR design. |
|---|---|
| AbstractList | DM-SMR adopts Persistent Cache (PC) to accommodate non-sequential write operations. However, the PC cleaning process induces severe long-tail latency. In this paper, we propose to mitigate the tail latency of PC cleaning by using Reinforcement Learning (RL). Specifically, a real-time lightweight Q-learning model is built to analyze the idle window of I/O workloads, based on which PC cleaning is judiciously scheduled, thereby maximally utilizing the I/O idle window and effectively hiding the tail latency from regular requests. We implement our technique inside a Linux device driver with an emulated SMR drive. Experimental results show that our technique can reduce the tail latency by 57.65% at 99.9th percentile and the average response time by 46.11% compared to a typical SMR design. |
| Author | Chang, Wanli Li, Bingzhe Shen, Zhaoyan Pan, Yungang Shao, Zili Jia, Zhiping |
| Author_xml | – sequence: 1 givenname: Yungang surname: Pan fullname: Pan, Yungang email: dlzrmr@mail.sdu.edu.cn organization: Shandong University,School of Computer Science and Technology,Qingdao,China – sequence: 2 givenname: Zhiping surname: Jia fullname: Jia, Zhiping email: jzp@sdu.edu.cn organization: Shandong University,School of Computer Science and Technology,Qingdao,China – sequence: 3 givenname: Zhaoyan surname: Shen fullname: Shen, Zhaoyan email: shenzhaoyan@sdu.edu.cn organization: Shandong University,School of Computer Science and Technology,Qingdao,China – sequence: 4 givenname: Bingzhe surname: Li fullname: Li, Bingzhe email: bingzhe.li@okstate.edu organization: Oklahoma State University,School of Electrical and Computer Engineering,Stillwater,OK,United States – sequence: 5 givenname: Wanli surname: Chang fullname: Chang, Wanli email: wanli.chang@york.ac.uk organization: Hunan University,College of Computer Science and Electronic Engineering,Changsha,China – sequence: 6 givenname: Zili surname: Shao fullname: Shao, Zili email: shao@cse.cuhk.edu.hk organization: The Chinese University of Hong Kong,Department of Computer Science and Engineering,Shatin,NT,Hong Kong |
| BookMark | eNotj9tKxDAYhCMoqGufQIS8QNf8aU69LF1P0CKsq7dLkv5ZA91U2t7s21txb2aY4WNgbsllGhIS8gBsDcDKx01Vg2FarDnjsC6lUcyIC5KV2oBSUhRcC3ZNsmmKjikmjVj0hnxtMaYwjB6PmGbaoB1TTIe8Wrhpxo7W1n8jrXu0fz2dB9rGOR7sjLQZFnBnY0-bJSZ_ojHRTZt_tNs7chVsP2F29hX5fH7a1a958_7yVldNbrnRc16oIJwK4IIrCimMDwgCuNOsUwvhApdeBqOl7LQO4K0GI7UoUWkAL8piRe7_dyMi7n_GeLTjaX9-X_wCWmxRVw |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/DAC18074.2021.9586084 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781665432740 1665432748 |
| EndPage | 108 |
| ExternalDocumentID | 9586084 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: 10.13039/501100001809 – fundername: National Science Foundation funderid: 10.13039/100000001 |
| GroupedDBID | 6IE 6IH ACM ALMA_UNASSIGNED_HOLDINGS CBEJK RIE RIO |
| ID | FETCH-LOGICAL-a287t-36f4b6f1bfb33548cfe1412b70d6a28bf25c5f8755d77f1ca7185749e6711c493 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000766079700018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:28:30 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a287t-36f4b6f1bfb33548cfe1412b70d6a28bf25c5f8755d77f1ca7185749e6711c493 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_9586084 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-Dec.-5 |
| PublicationDateYYYYMMDD | 2021-12-05 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-Dec.-5 day: 05 |
| PublicationDecade | 2020 |
| PublicationTitle | 2021 58th ACM/IEEE Design Automation Conference (DAC) |
| PublicationTitleAbbrev | DAC |
| PublicationYear | 2021 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssib060584060 |
| Score | 2.2329888 |
| Snippet | DM-SMR adopts Persistent Cache (PC) to accommodate non-sequential write operations. However, the PC cleaning process induces severe long-tail latency. In this... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 103 |
| SubjectTerms | Cleaning Cleaning Process Design automation Idle Time Window Linux Real-time systems Reinforcement learning Shingled Magnetic Recording Tail-Latency Time factors Timing |
| Title | Reinforcement Learning-Assisted Cache Cleaning to Mitigate Long-Tail Latency in DM-SMR |
| URI | https://ieeexplore.ieee.org/document/9586084 |
| WOSCitedRecordID | wos000766079700018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxFAxt8eBJpRW_ycGjaZP9yuYoW4uHtpRapbeSZF9koexK3Qr-e5N0rQhevIWQEJiEvDdJZoLQLUulYDoGYmz2TZz4kcjEREQHkkJgLN0GLxQe8-k0XS7FrIXu9loYAPCPz6Dviv4uP6_01h2VDUScJjSN2qjNebLTan2vHXe7Z2MTbUQ6jIrB8D5jzurFksCA9Zu-vz5R8TFkdPS_0Y9R70eMh2f7MHOCWlB20cscvOWp9qd7uHFJfSUWbTdvOc6cUTPO1iBdPa4rPCm8nQbgcWUbLmSxxmPpMuZPXJR4OCFPk3kPPY8eFtkjab5IINJSnZqEFleVGKaMCkNLPrQBFrFAcZontoUyQaxjYzlJnHNumJbceT9FAhLOmI5EeIo6ZVXCGcJRKKk0VKXa5lhKJCI0nrzFqe1kd4Vz1HWYrN52LhirBo6Lv6sv0aGD3T_8iK9Qp95s4Rod6I-6eN_c-Kn7AkjMmKQ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA5zCnpS2cTf5uDRbEmbtM1ROmViO8acsttI00QKo5XZCf73JlmdCF68hZAQeC_kvS_J9z0ArkkkOJFMIW2yb2TJj0gEmiLpCaw8beC2ckThJByNotmMj1vgZsOFUUq5z2eqZ5vuLT-v5MpelfU5iwIc0S2wzSj18Jqt9b177PueiU64oekQzPuD25hYsRcDAz3Sa2b_KqPiosj9_v_WPwDdHzoeHG8CzSFoqbIDXibKiZ5Kd78HG53UV2TsbT2Xw9hKNcN4oYTth3UF08IJaiiYVGbgVBQLmAibM3_CooSDFD2lky54vr-bxkPUFElAwoCdGvnGslmgSaYz3zfwQ2pFKPGyEOeBGZFpj0mmDSpheRhqIkVo1Z8oV0FIiKTcPwLtsirVMYDUF1honEXSZFkZD7ivHXxjkZlkzoUT0LE2mb-tdTDmjTlO_-6-ArvDaZrMk4fR4xnYsy5w30DYOWjXy5W6ADvyoy7el5fOjV9sL5vr |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2021+58th+ACM%2FIEEE+Design+Automation+Conference+%28DAC%29&rft.atitle=Reinforcement+Learning-Assisted+Cache+Cleaning+to+Mitigate+Long-Tail+Latency+in+DM-SMR&rft.au=Pan%2C+Yungang&rft.au=Jia%2C+Zhiping&rft.au=Shen%2C+Zhaoyan&rft.au=Li%2C+Bingzhe&rft.date=2021-12-05&rft.pub=IEEE&rft.spage=103&rft.epage=108&rft_id=info:doi/10.1109%2FDAC18074.2021.9586084&rft.externalDocID=9586084 |