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Archit. Code Optim."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\u2018\u2018Learned\u201d admission policies have shown promise in improving Content Delivery Network (CDN) cache performance and lowering operational costs. Unfortunately, existing learned policies are optimized with a few fixed cache sizes while in reality, cache sizes often vary over time in an unpredictable manner. As a result, existing solutions cannot provide consistent benefits in production settings.<\/jats:p>\n          <jats:p>\n            We present\n            <jats:italic>SLAP<\/jats:italic>\n            , a learned CDN cache admission approach based on segmented object reuse time prediction.\n            <jats:italic>SLAP<\/jats:italic>\n            predicts an object\u2019s reuse time range using the Long-Short-Term-Memory model and admits objects that will be reused (before eviction) given the current cache size.\n            <jats:italic>SLAP<\/jats:italic>\n            decouples model training from cache size, allowing it to adapt to arbitrary sizes. The key to our solution is a novel segmented labeling scheme that makes\n            <jats:italic>SLAP<\/jats:italic>\n            without requiring precise prediction on object reuse time. To further make\n            <jats:italic>SLAP<\/jats:italic>\n            a practical and efficient solution, we propose aggressive reusing of computation and training on sampled traces to optimize model training, and a specialized predictor architecture that overlaps prediction computation with miss object fetching to optimize model inference. Our experiments using production CDN traces show that SLAP achieves significantly lower write traffic (38%-59%), longer SSDs lifetime (104%-178%), a consistently higher hit rate (3.2%-11.7%), and requires no effort to adapt to changing cache sizes, outperforming existing policies.\n          <\/jats:p>","DOI":"10.1145\/3646550","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T11:54:26Z","timestamp":1707479666000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["SLAP: Segmented Reuse-Time-Label Based Admission Policy for Content Delivery Network Caching"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2120-3168","authenticated-orcid":false,"given":"Ke","family":"Liu","sequence":"first","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2004-7193","authenticated-orcid":false,"given":"Kan","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, Madison, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-7322","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2161-8796","authenticated-orcid":false,"given":"Ke","family":"Zhou","sequence":"additional","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4972-1231","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3770-1463","authenticated-orcid":false,"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3828-5148","authenticated-orcid":false,"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"Tencent Technology (Shenzhen) Co., Ltd., Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"[n.d.]. https:\/\/www.cloudflare.com\/learning\/cdn\/performance\/"},{"key":"e_1_3_2_3_2","unstructured":"[n.d.]. https:\/\/ftp.pdl.cmu.edu\/pub\/datasets\/twemcacheWorkload\/cacheDatasets\/"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2011.07.015"},{"key":"e_1_3_2_5_2","volume-title":"Introduction to Machine Learning","author":"Alpaydin Ethem","year":"2020","unstructured":"Ethem Alpaydin. 2020. 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