CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing
Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of experience (QoE). Existing content caching techniques att...
Gespeichert in:
Veröffentlicht in: | Cluster computing 2023-02, Vol.26 (1), p.267-281 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 281 |
---|---|
container_issue | 1 |
container_start_page | 267 |
container_title | Cluster computing |
container_volume | 26 |
creator | Yasir, Muhammad uz Zaman, Sardar Khaliq Maqsood, Tahir Rehman, Faisal Mustafa, Saad |
description | Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users’ preferences while estimating the popularity of content. Knowing users’ preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users’ preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques. |
doi_str_mv | 10.1007/s10586-022-03624-0 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2918250265</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918250265</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-aa82f022c774e563ef8dcbb1b550343fe85c464c1f467e7ec350621d41f624963</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EEqXwA6wssQ747YQdqnhJleiCriPHGYeU1gl2ooq_xyUIdmxmRpozj3sRuqTkmhKibyIlMlcZYSwjXDGRkSM0o1LzTEvBj1PNU1vnUp-isxg3hJBCs2KGmkW3Wq9use38AH7AfdePWxPa4RMbX-MxQsB9AAcBvIWIzd4E-KWtsW-tb7ALZgf7Lrzj1uNdV7VbwFA3B3DXj0NCztGJM9sIFz95jtYP96-Lp2z58vi8uFtmltNiyIzJmUsqrNYCpOLg8tpWFa2kJFxwB7m0QglLnVAaNFguiWK0FtQl1YXic3Q17e1D9zFCHMpNNwafTpasoDmThCmZKDZRNnQxJnllH9qdCZ8lJeXB0HIytEyvlN-GpjhHfBqKCfYNhL_V_0x9Ab5LeWU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2918250265</pqid></control><display><type>article</type><title>CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing</title><source>SpringerNature Journals</source><source>ProQuest Central UK/Ireland</source><source>ProQuest Central</source><creator>Yasir, Muhammad ; uz Zaman, Sardar Khaliq ; Maqsood, Tahir ; Rehman, Faisal ; Mustafa, Saad</creator><creatorcontrib>Yasir, Muhammad ; uz Zaman, Sardar Khaliq ; Maqsood, Tahir ; Rehman, Faisal ; Mustafa, Saad</creatorcontrib><description>Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users’ preferences while estimating the popularity of content. Knowing users’ preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users’ preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques.</description><identifier>ISSN: 1386-7857</identifier><identifier>EISSN: 1573-7543</identifier><identifier>DOI: 10.1007/s10586-022-03624-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial neural networks ; Caching ; Computer Communication Networks ; Computer Science ; Edge computing ; Internet of Things ; Internet service providers ; Machine learning ; Mobile computing ; Neural networks ; Operating Systems ; Processor Architectures ; Response time ; Social networks ; Streaming media ; User experience</subject><ispartof>Cluster computing, 2023-02, Vol.26 (1), p.267-281</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-aa82f022c774e563ef8dcbb1b550343fe85c464c1f467e7ec350621d41f624963</citedby><cites>FETCH-LOGICAL-c319t-aa82f022c774e563ef8dcbb1b550343fe85c464c1f467e7ec350621d41f624963</cites><orcidid>0000-0003-0439-5700</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10586-022-03624-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918250265?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,782,786,21395,27931,27932,33751,41495,42564,43812,51326,64392,64396,72476</link.rule.ids></links><search><creatorcontrib>Yasir, Muhammad</creatorcontrib><creatorcontrib>uz Zaman, Sardar Khaliq</creatorcontrib><creatorcontrib>Maqsood, Tahir</creatorcontrib><creatorcontrib>Rehman, Faisal</creatorcontrib><creatorcontrib>Mustafa, Saad</creatorcontrib><title>CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing</title><title>Cluster computing</title><addtitle>Cluster Comput</addtitle><description>Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users’ preferences while estimating the popularity of content. Knowing users’ preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users’ preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques.</description><subject>Artificial neural networks</subject><subject>Caching</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Edge computing</subject><subject>Internet of Things</subject><subject>Internet service providers</subject><subject>Machine learning</subject><subject>Mobile computing</subject><subject>Neural networks</subject><subject>Operating Systems</subject><subject>Processor Architectures</subject><subject>Response time</subject><subject>Social networks</subject><subject>Streaming media</subject><subject>User experience</subject><issn>1386-7857</issn><issn>1573-7543</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtOwzAQRS0EEqXwA6wssQ747YQdqnhJleiCriPHGYeU1gl2ooq_xyUIdmxmRpozj3sRuqTkmhKibyIlMlcZYSwjXDGRkSM0o1LzTEvBj1PNU1vnUp-isxg3hJBCs2KGmkW3Wq9use38AH7AfdePWxPa4RMbX-MxQsB9AAcBvIWIzd4E-KWtsW-tb7ALZgf7Lrzj1uNdV7VbwFA3B3DXj0NCztGJM9sIFz95jtYP96-Lp2z58vi8uFtmltNiyIzJmUsqrNYCpOLg8tpWFa2kJFxwB7m0QglLnVAaNFguiWK0FtQl1YXic3Q17e1D9zFCHMpNNwafTpasoDmThCmZKDZRNnQxJnllH9qdCZ8lJeXB0HIytEyvlN-GpjhHfBqKCfYNhL_V_0x9Ab5LeWU</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Yasir, Muhammad</creator><creator>uz Zaman, Sardar Khaliq</creator><creator>Maqsood, Tahir</creator><creator>Rehman, Faisal</creator><creator>Mustafa, Saad</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0003-0439-5700</orcidid></search><sort><creationdate>20230201</creationdate><title>CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing</title><author>Yasir, Muhammad ; uz Zaman, Sardar Khaliq ; Maqsood, Tahir ; Rehman, Faisal ; Mustafa, Saad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-aa82f022c774e563ef8dcbb1b550343fe85c464c1f467e7ec350621d41f624963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial neural networks</topic><topic>Caching</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Edge computing</topic><topic>Internet of Things</topic><topic>Internet service providers</topic><topic>Machine learning</topic><topic>Mobile computing</topic><topic>Neural networks</topic><topic>Operating Systems</topic><topic>Processor Architectures</topic><topic>Response time</topic><topic>Social networks</topic><topic>Streaming media</topic><topic>User experience</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yasir, Muhammad</creatorcontrib><creatorcontrib>uz Zaman, Sardar Khaliq</creatorcontrib><creatorcontrib>Maqsood, Tahir</creatorcontrib><creatorcontrib>Rehman, Faisal</creatorcontrib><creatorcontrib>Mustafa, Saad</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Cluster computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yasir, Muhammad</au><au>uz Zaman, Sardar Khaliq</au><au>Maqsood, Tahir</au><au>Rehman, Faisal</au><au>Mustafa, Saad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing</atitle><jtitle>Cluster computing</jtitle><stitle>Cluster Comput</stitle><date>2023-02-01</date><risdate>2023</risdate><volume>26</volume><issue>1</issue><spage>267</spage><epage>281</epage><pages>267-281</pages><issn>1386-7857</issn><eissn>1573-7543</eissn><abstract>Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users’ preferences while estimating the popularity of content. Knowing users’ preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users’ preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10586-022-03624-0</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-0439-5700</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1386-7857 |
ispartof | Cluster computing, 2023-02, Vol.26 (1), p.267-281 |
issn | 1386-7857 1573-7543 |
language | eng |
recordid | cdi_proquest_journals_2918250265 |
source | SpringerNature Journals; ProQuest Central UK/Ireland; ProQuest Central |
subjects | Artificial neural networks Caching Computer Communication Networks Computer Science Edge computing Internet of Things Internet service providers Machine learning Mobile computing Neural networks Operating Systems Processor Architectures Response time Social networks Streaming media User experience |
title | CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-05T10%3A58%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=CoPUP:%20content%20popularity%20and%20user%20preferences%20aware%20content%20caching%20framework%20in%20mobile%20edge%20computing&rft.jtitle=Cluster%20computing&rft.au=Yasir,%20Muhammad&rft.date=2023-02-01&rft.volume=26&rft.issue=1&rft.spage=267&rft.epage=281&rft.pages=267-281&rft.issn=1386-7857&rft.eissn=1573-7543&rft_id=info:doi/10.1007/s10586-022-03624-0&rft_dat=%3Cproquest_cross%3E2918250265%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2918250265&rft_id=info:pmid/&rfr_iscdi=true |