dc.contributor.author | Zhang, X | |
dc.contributor.author | Qi, Z | |
dc.contributor.author | Min, G | |
dc.contributor.author | Miao, W | |
dc.contributor.author | Fan, Q | |
dc.contributor.author | Ma, Z | |
dc.date.accessioned | 2021-12-13T09:01:57Z | |
dc.date.issued | 2021-12-14 | |
dc.date.updated | 2021-12-11T00:12:41Z | |
dc.description.abstract | With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching schemes do not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve this problem, we design a new learning-based cooperative caching strategy (LECS) that encompasses three key components. Firstly, a temporal convolutional network driven content popularity prediction model is developed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content cached on a given edge server. Thirdly, an novel dynamic programming algorithm is developed to maximize the overall CCV. Extensive simulation results have demonstrated the superiority of our approach. Compared with the state-of-the-art caching schemes, LECS can improve the cache hit rate by 8.3%-10.1%, and reduce the average content delivery delay by 9.1%-15.1%. | en_GB |
dc.description.sponsorship | National Key Research and Development Program of China | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | Natural Science Foundation of Jiangsu | en_GB |
dc.description.sponsorship | Leading Technology of Jiangsu Basic Research Plan | en_GB |
dc.description.sponsorship | Chongqing Key Laboratory of Digital Cinema Art Theory and Technology | en_GB |
dc.identifier.citation | Published online 14 December 2021 | en_GB |
dc.identifier.doi | 10.1109/TPDS.2021.3135257 | |
dc.identifier.grantnumber | 2018YFB2100804 | en_GB |
dc.identifier.grantnumber | 898588 | en_GB |
dc.identifier.grantnumber | 101008297 | en_GB |
dc.identifier.grantnumber | 61902178 | en_GB |
dc.identifier.grantnumber | 92067206 | en_GB |
dc.identifier.grantnumber | 62102053 | en_GB |
dc.identifier.grantnumber | 61972222 | en_GB |
dc.identifier.grantnumber | BK20190295 | en_GB |
dc.identifier.grantnumber | BK20192003 | en_GB |
dc.identifier.grantnumber | 2021KF01 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/128107 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2021 IEEE | |
dc.subject | Cooperative Edge Caching | en_GB |
dc.subject | Temporal Convolutional Networks | en_GB |
dc.subject | Content Caching Value | en_GB |
dc.subject | Content Popularity | en_GB |
dc.title | Cooperative Edge Caching Based on Temporal Convolutional Networks | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-12-13T09:01:57Z | |
dc.identifier.issn | 1558-2183 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Parallel and Distributed Systems | en_GB |
dc.relation.ispartof | IEEE Transactions on Parallel and Distributed Systems | |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2021-11-25 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-11-25 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-12-11T00:13:14Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-01-05T13:21:32Z | |
refterms.panel | B | en_GB |