乐胖代购免代理版

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It comprises of two main components: i)\n Object Characteristics Predictor,<\/jats:italic>\n which builds upon deep LSTM Encoder-Decoder model to predict the future characteristics of an object (such as object popularity) - to the best of our knowledge, we are the first to propose LSTM Encoder-Decoder model for content caching; ii)\n a caching policy component,<\/jats:italic>\n which accounts for predicted information of objects to make smart caching decisions. In our thorough experiments, we show that applying Deepcache Framework to existing cache policies, such as LRU and k-LRU, significantly boosts the number of cache hits.\n <\/jats:p>","DOI":"10.1145\/3310165.3310174","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T13:16:22Z","timestamp":1548767782000},"page":"64-69","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Making content caching policies 'smart' using the deepcache framework"],"prefix":"10.1145","volume":"48","author":[{"given":"Arvind","family":"Narayanan","sequence":"first","affiliation":[{"name":"University of Minnesota"}]},{"given":"Saurabh","family":"Verma","sequence":"additional","affiliation":[{"name":"University of Minnesota"}]},{"given":"Eman","family":"Ramadan","sequence":"additional","affiliation":[{"name":"University of Minnesota"}]},{"given":"Pariya","family":"Babaie","sequence":"additional","affiliation":[{"name":"University of Minnesota"}]},{"given":"Zhi-Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Minnesota"}]}],"member":"320","published-online":{"date-parts":[[2019,1,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Forecast and methodology","author":"Cisco","year":"2016","unstructured":"Cisco visual networking index : Forecast and methodology , 2016 --2021, 2017. 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