{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/work-in-progress-a-deep-learning-strategy-for","title":"Work-in-progress: a deep learning strategy for I/O scheduling in storage systems","arxiv_id":null,"date":"2019-12-03","proceeding":"IEEE Real-Time Systems Symposium (RTSS) 2019 12","authors":["Ashkan Farhangi","Jiang Bian","Jun Wang","Zhishan Guo"],"abstract":"Under the big data era, there is a crucial need to improve the performance of storage systems for data-intensive applications. Data-intensive applications tend to behave in a predictable manner, which can be exploited for improving the performance of the storage system. At the storage level, we propose a deep recurrent neural network that learns the patterns of I/O requests and predicts the upcoming ones, such that memory contents can be pre-loaded at the right time to prevent cache/memory misses. Preliminary experimental results, on two real-world I/O logs of storage systems (from financial and web search), are reported-they partially demonstrate the effectiveness of the proposed method.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9052144/","url_pdf":"https://www.researchgate.net/profile/Ashkan-Farhangi/publication/340401542_Work-in-Progress_A_Deep_Learning_Strategy_for_IO_Scheduling_in_Storage_Systems/links/61f05414dafcdb25fd4ea659/Work-in-Progress-A-Deep-Learning-Strategy-for-I-O-Scheduling-in-Storage-Systems.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"work-in-progress-a-deep-learning-strategy-for","repo_url":"https://github.com/ashfarhangi/DeepIOScheduler","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"hardware-aware-neural-architecture-search","task_name":"Hardware Aware Neural Architecture Search"},{"task_slug":"irregular-time-series","task_name":"Irregular Time Series"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}