{"id":1017003,"date":"2024-03-20T13:19:40","date_gmt":"2024-03-20T20:19:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1017003"},"modified":"2024-03-20T13:19:40","modified_gmt":"2024-03-20T20:19:40","slug":"baleen-ml-admission-prefetching-for-flash-caches","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/baleen-ml-admission-prefetching-for-flash-caches\/","title":{"rendered":"Baleen: ML Admission & Prefetching for Flash Caches"},"content":{"rendered":"

Flash caches are used to reduce peak backend load for throughput-constrained data center services, reducing the total number of backend servers required. Bulk storage systems are a large-scale example, backed by high-capacity but low-throughput hard disks, and using flash caches to provide a more cost-effective storage layer underlying everything from blobstores to data warehouses.<\/p>\n

However, flash caches must address the limited write endurance of flash by limiting the long-term average flash write rate to avoid premature wearout. To do so, most flash caches must use admission policies to filter cache insertions and maximize the workload-reduction value of each flash write.<\/p>\n

The Baleen flash cache uses coordinated ML admission and prefetching to reduce peak backend load. After learning painful lessons with our early ML policy attempts, we exploit a new cache residency model (which we call episodes) to guide model training. We focus on optimizing for an end-toend system metric (Disk-head Time) that measures backend load more accurately than IO miss rate or byte miss rate.
\nEvaluation using Meta traces from seven storage clusters shows that Baleen reduces Peak Disk-head Time (and hence the number of backend hard disks required) by 12% over stateof-the-art policies for a fixed flash write rate constraint. BaleenTCO, which chooses an optimal flash write rate, reduces our estimated total cost of ownership (TCO) by 17%. Code and traces are available via https:\/\/www.pdl.cmu.edu\/CILES\/.<\/p>\n","protected":false},"excerpt":{"rendered":"

Flash caches are used to reduce peak backend load for throughput-constrained data center services, reducing the total number of backend servers required. Bulk storage systems are a large-scale example, backed by high-capacity but low-throughput hard disks, and using flash caches to provide a more cost-effective storage layer underlying everything from blobstores to data warehouses. However, […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[246691],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1017003","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us","msr-field-of-study-computer-science"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2024-2-28","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"USENIX","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.usenix.org\/system\/files\/fast24-wong.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Daniel Lin-Kit Wong","user_id":0,"rest_url":false},{"type":"text","value":"Hao Wu","user_id":0,"rest_url":false},{"type":"text","value":"Carson Molder","user_id":0,"rest_url":false},{"type":"text","value":"S. Gunasekar","user_id":0,"rest_url":false},{"type":"text","value":"Jimmy Lu","user_id":0,"rest_url":false},{"type":"text","value":"Snehal Khandkar","user_id":0,"rest_url":false},{"type":"text","value":"Abhinav Sharma","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Daniel S. Berger","user_id":38892,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Daniel S. Berger"},{"type":"text","value":"Nathan Beckmann","user_id":0,"rest_url":false},{"type":"text","value":"G. R. 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