Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81787
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dc.contributorDepartment of Computing-
dc.creatorZhang, Cen_US
dc.creatorPang, HTen_US
dc.creatorLiu, JCen_US
dc.creatorTang, SZen_US
dc.creatorZhang, RXen_US
dc.creatorWang, Den_US
dc.creatorSun, LFen_US
dc.date.accessioned2020-02-10T12:29:11Z-
dc.date.available2020-02-10T12:29:11Z-
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10397/81787-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication C. Zhang et al., "Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory Learning," in IEEE Access, vol. 7, pp. 152832-152846, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2947067en_US
dc.subjectEdge-assisted caching replacementen_US
dc.subjectIntelligent content cachingen_US
dc.subjectLong short term memoryen_US
dc.titleToward edge-assisted video content intelligent caching with long short-term memory learningen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage152832en_US
dc.identifier.epage152846en_US
dc.identifier.volume7en_US
dc.identifier.doi10.1109/ACCESS.2019.2947067en_US
dcterms.abstractNowadays video content has contributed to the majority of Internet traffic, which brings great challenge to the network infrastructure. Fortunately, the emergence of edge computing has provided a promising way to reduce the video load on the network by caching contents closer to users.But caching replacement algorithm is essential for the cache efficiency considering the limited cache space under existing edge-assisted network architecture. To investigate the challenges and opportunities inside, we first measure the performance of five state-of-the-art caching algorithms based on three real-world datasets. Our observation shows that state-of-the-art caching replacement algorithms suffer from following weaknesses: 1) the rule-based replacement approachs (e.g., LFU,LRU) cannot adapt under different scenarios; 2) data-driven forecast approaches only work efficiently on specific scenarios or datasets, as the extracted features working on one dataset may not work on another one. Motivated by these observations and edge-assisted computation capacity, we then propose an edge-assisted intelligent caching replacement framework <italic>LSTM-C</italic> based on deep Long Short-Term Memory network, which contains two types of modules: 1) four basic modules manage the coordination among content requests, content replace, cache space, service management; 2) three learning-based modules enable the online deep learning to provide intelligent caching strategy. Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. Our experiment results show that LSTM-C outperforms state-of-the-art methods in cache hit rate on three real-traces of video requests. When the cache size is limited, LSTM-C outperforms baselines by on average respectively, which are fast enough for online operations.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 11 Oct. 2019, v. 7, p. 152832-152846en_US
dcterms.isPartOfIEEE accessen_US
dcterms.issued2019-
dc.identifier.isiWOS:000497163000197-
dc.identifier.scopus2-s2.0-85078492655-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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