Performance of cache placement using supervised learning techniques in mobile edge networks
Abstract With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimise the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges with respect to the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-11-01
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| Series: | IET Networks |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ntw2.12029 |
| _version_ | 1798018890854301696 |
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| author | Lubna Mohammed Alagan Anpalagan Ahmed S. Khwaja Muhammad Jaseemuddin |
| author_facet | Lubna Mohammed Alagan Anpalagan Ahmed S. Khwaja Muhammad Jaseemuddin |
| author_sort | Lubna Mohammed |
| collection | DOAJ |
| description | Abstract With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimise the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges with respect to the decision of cache contents. Here, a multi‐objective cache content strategy that aims to maximise the cache hit rate of SBSs in mobile edge networks (MENs) is proposed. The multi‐objective cache placement optimisation is formulated as a classification problem. Unlike previous work, mobility input attributes such as user locations, contact duration, communication ranges, contact probability between UTs and SBSs, etc. as well as content popularity and the correlation between these input attributes separating the decision space into two regions of cache and not cache are used. Stochastic gradient descent algorithm is used for the training of three supervised machine learning techniques: artificial neural network ANN, support vector machine (SVM), and logistic regression LR to define the hyperplane that separates the cache content decision space. Simulation results show that compared with the weighted‐sum approach, the SBSs cache hit rates increase on the average by 18.58%, 18.52%, and 18.2%, and the total energy consumption values decrease on the average by 33.49%, 53.19%, and 49.9% for ANN, SVM, and LR, respectively. |
| first_indexed | 2024-04-11T16:31:39Z |
| format | Article |
| id | doaj.art-a32f9bdb607248c180562927336982a1 |
| institution | Directory Open Access Journal |
| issn | 2047-4954 2047-4962 |
| language | English |
| last_indexed | 2024-04-11T16:31:39Z |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Networks |
| spelling | doaj.art-a32f9bdb607248c180562927336982a12022-12-22T04:14:01ZengWileyIET Networks2047-49542047-49622021-11-0110630432110.1049/ntw2.12029Performance of cache placement using supervised learning techniques in mobile edge networksLubna Mohammed0Alagan Anpalagan1Ahmed S. Khwaja2Muhammad Jaseemuddin3Department of Electrical, Computer and Biomedical, Engineering Ryerson University Toronto CanadaDepartment of Electrical, Computer and Biomedical, Engineering Ryerson University Toronto CanadaDepartment of Electrical, Computer and Biomedical, Engineering Ryerson University Toronto CanadaDepartment of Electrical, Computer and Biomedical, Engineering Ryerson University Toronto CanadaAbstract With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimise the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges with respect to the decision of cache contents. Here, a multi‐objective cache content strategy that aims to maximise the cache hit rate of SBSs in mobile edge networks (MENs) is proposed. The multi‐objective cache placement optimisation is formulated as a classification problem. Unlike previous work, mobility input attributes such as user locations, contact duration, communication ranges, contact probability between UTs and SBSs, etc. as well as content popularity and the correlation between these input attributes separating the decision space into two regions of cache and not cache are used. Stochastic gradient descent algorithm is used for the training of three supervised machine learning techniques: artificial neural network ANN, support vector machine (SVM), and logistic regression LR to define the hyperplane that separates the cache content decision space. Simulation results show that compared with the weighted‐sum approach, the SBSs cache hit rates increase on the average by 18.58%, 18.52%, and 18.2%, and the total energy consumption values decrease on the average by 33.49%, 53.19%, and 49.9% for ANN, SVM, and LR, respectively.https://doi.org/10.1049/ntw2.12029cache storageenergy consumptiongradient methodslearning (artificial intelligence)neural netsoptimisation |
| spellingShingle | Lubna Mohammed Alagan Anpalagan Ahmed S. Khwaja Muhammad Jaseemuddin Performance of cache placement using supervised learning techniques in mobile edge networks IET Networks cache storage energy consumption gradient methods learning (artificial intelligence) neural nets optimisation |
| title | Performance of cache placement using supervised learning techniques in mobile edge networks |
| title_full | Performance of cache placement using supervised learning techniques in mobile edge networks |
| title_fullStr | Performance of cache placement using supervised learning techniques in mobile edge networks |
| title_full_unstemmed | Performance of cache placement using supervised learning techniques in mobile edge networks |
| title_short | Performance of cache placement using supervised learning techniques in mobile edge networks |
| title_sort | performance of cache placement using supervised learning techniques in mobile edge networks |
| topic | cache storage energy consumption gradient methods learning (artificial intelligence) neural nets optimisation |
| url | https://doi.org/10.1049/ntw2.12029 |
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