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Code Optim."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>With the sheer volume of data in today\u2019s world, archive storage systems play a significant role in persisting the cold data. Due to stringent cost concerns, one popular design is to organize disks into groups and periodically switch them to be powered on for serving user requests. Scheduling thus becomes critical for both CapEx and performance. Unfortunately, field results indicate that existing schedulers can be often suboptimal. Our further analysis suggests that the main reason is the mismatch between the ever-changing workloads and the fixed set of coarsely-configured parameters in current heuristic-based schedulers.<\/jats:p>\n          <jats:p>\n            In this article, we propose\n            <jats:monospace>MasterPlan<\/jats:monospace>\n            , a reinforcement learning (RL) based scheduler for archive storage systems. By identifying the unique characteristics of archive storage service, we design a state space and reward function for the RL agent.\n            <jats:monospace>MasterPlan<\/jats:monospace>\n            includes a continuous action encoding approach to guarantee efficient exploration, and a meta adaptation module to extract features of workload series. Experiments show that\n            <jats:monospace>MasterPlan<\/jats:monospace>\n            can achieve 1.25\u00d7 throughput, 2.16\u00d7 99\n            <jats:sup>th<\/jats:sup>\n            latency and 1.47\u00d7 power draw improvement compared to existing solutions.\n          <\/jats:p>","DOI":"10.1145\/3708542","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T11:24:14Z","timestamp":1734434654000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MasterPlan: A Reinforcement Learning Based Scheduler for Archive Storage"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5567-1560","authenticated-orcid":false,"given":"Xinqi","family":"Chen","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6654-4364","authenticated-orcid":false,"given":"Erci","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-4537","authenticated-orcid":false,"given":"Dengyao","family":"Mo","sequence":"additional","affiliation":[{"name":"Alibaba Cloud Computing, Bellevue, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4236-289X","authenticated-orcid":false,"given":"Ruiming","family":"Lu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8933-8572","authenticated-orcid":false,"given":"Haonan","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2190-0919","authenticated-orcid":false,"given":"Dian","family":"Ding","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1617-3593","authenticated-orcid":false,"given":"Guangtao","family":"Xue","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2024. 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