{"id":943,"date":"2021-01-27T11:42:59","date_gmt":"2021-01-27T10:42:59","guid":{"rendered":"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defense-probabilistic-content-popularity-learning-in-proactive-caching-systems\/"},"modified":"2021-01-27T11:42:59","modified_gmt":"2021-01-27T10:42:59","slug":"phd-defense-probabilistic-content-popularity-learning-in-proactive-caching-systems","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defense-probabilistic-content-popularity-learning-in-proactive-caching-systems\/","title":{"rendered":"PhD Defense: Probabilistic Content Popularity Learning in Proactive Caching Systems"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p><strong>Please click on <a href=\"https:\/\/unilu.webex.com\/unilu\/onstage\/g.php?MTID=ef334523087d3f459b8ee097996d5b400\" target=\"_blank\" title=\"\" rel=\"noopener\">this link<\/a> and join the online PhD defense.<\/strong><\/p><p><strong>Members of the defense committee<\/strong>:<\/p><ul class=\"ulux-list\"><li class=\"ulux-list-item\">Assistant Prof. Dr. Djamila AOUADA, University of Luxembourg, Chairman<\/li><li class=\"ulux-list-item\">Prof. Dr. Symeon CHATZINOTAS, University of Luxembourg, Deputy Chairman<\/li><li class=\"ulux-list-item\">Prof. Dr. Bj\u00f6rn OTTERSTEN, University of Luxembourg, Supervisor<\/li><li class=\"ulux-list-item\">Prof. Dr. Saikat CHATTERJEE, Royal Institute of Technology, Sweden, Member<\/li><li class=\"ulux-list-item\">Prof. Dr. Muhammad IMRAN, University of Glasgow, Scotland , Member<\/li><\/ul><p><strong>Abstract<\/strong>:<\/p><p>Recent rapid growth of data traffic in mobile networks has stretched the capability of current network architectures. Proactively caching popular contents close to end-users has been proposed as a promising approach to mitigate the issue.<\/p><p>A full-fledged proactive cache management mechanism encompasses two interrelated algorithms which need to be carefully designed:\u00a0 content popularity prediction and caching policy.\u00a0Abundant research has focused on the performance of various caching policies assuming that the content popularity is perfectly known. Nonetheless,\u00a0 the content popularity is unknown in practice and has to be predicted from users&rsquo; requests.\u00a0 Due to non-deterministic and time-varying nature of the requests, the prediction is nontrivial. In this thesis, the main focus is to introduce efficient prediction algorithms from Bayesian viewpoint. The Bayesian approach provides a powerful framework to construct statistical models which capture uncertainty and\u00a0 are robust to \u00ab\u00a0over-fitting\u00a0\u00bb issue. <\/p><p>Firstly, we consider the prediction problem under stationary scenario.\u00a0 To enhance the accuracy of\u00a0 prediction, content features are leveraged and a Bayesian Poisson regressor based on a Gaussian process is proposed. The model can automatically discover hidden patterns in the feature space among the already-existing or textit{seen} contents. It also allows to predict the popularities of newly-added or textit{unseen} contents whose statistical data is not available in advance. We show that these capabilities of the model can have significant impact on\u00a0 caching performance.\u00a0\u00a0 <\/p><p>Secondly, we formulate a cooperative content caching in order to optimize the aggregated network cost for delivering contents to users. An efficient caching policy requires an accurate prediction of time varying content popularity. The requests can potentially have interactions over time, among contents, and across locations.\u00a0 To exploit these patterns, a probabilistic dynamical model based on a canonical tensor decomposition\u00a0 is developed. Additionally, an online learning method that works with streaming data where content request arrives sequentially is designed. Numerical results confirm that modeling time-content-location interactions by the proposed model can improve the cooperative caching strategy performance.<\/p><p>Last but not least, we take one step further and develop a dynamical model, which besides time-content-location interactions, it\u00a0 can also uncover a\u00a0 non-linear temporal trend structure in content requests through which a more accurate prediction can be attained.\u00a0 Subsequently, a cooperative caching policy is designed which adaptively performs network resource allocation and optimizes\u00a0 content delivery according to the dynamic of content requests.\u00a0 Therefore, the policy provides a more efficient utilization of network resources.\u00a0 Using simulations, we show that the developed\u00a0 caching mechanism\u00a0 outperforms reference methods which ignore the temporal trend information.<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":0,"featured_media":944,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2021-02-17 14:00:00","event_end_date":"2021-02-17 16:00:00","event_speaker_name":"Sajad Mehrizi","event_speaker_link":"","event_is_online":false,"event_location":"","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[],"events-type":[],"organisation":[183],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - 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