IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING
The application of computing resources through mobile devices (MDs) is called Mobile Computing; between cloud datacentres and devices, it is known as (Mobile) Fog Computing (MFC). We ran Cloudsim simulator to offload tasks in suitable Fog Devices (FDs), cloud or mobile. We stored the outputs of the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
2020-12-01
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| Series: | Jordanian Journal of Computers and Information Technology |
| Subjects: | |
| Online Access: | https://jjcit.org/paper/112 |
| _version_ | 1828123551598641152 |
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| author | Elham Darbanian Dadmehr Rahbari Roghayeh Ghanizadeh Mohsen Nickray |
| author_facet | Elham Darbanian Dadmehr Rahbari Roghayeh Ghanizadeh Mohsen Nickray |
| author_sort | Elham Darbanian |
| collection | DOAJ |
| description | The application of computing resources through mobile devices (MDs) is called Mobile Computing; between cloud datacentres and devices, it is known as (Mobile) Fog Computing (MFC). We ran Cloudsim simulator to offload tasks in suitable Fog Devices (FDs), cloud or mobile. We stored the outputs of the simulator as a dataset with features and a target class. A target class is a device in which tasks are offloaded and features of tasks are authentication, confidentiality, integrity, availability, capacity, speed and cost. Decision Tree (DT), Random Forest (RF), Extra-trees and AdaBoost classifiers were classified based on attribute values and the plot of trees was drawn. According to the plot of these classifiers, we extracted each sequential condition from root to leaves and inserted it into the simulator. What these classifiers do is to improve the conditions that should be inserted in the corresponding section of the simulator. We improved the response time of offloading by Random Forest, Extra-trees and AdaBoost over Decision Tree. |
| first_indexed | 2024-04-11T14:53:19Z |
| format | Article |
| id | doaj.art-e586b8f2c47c4dd59e3eb59ddbeccf58 |
| institution | Directory Open Access Journal |
| issn | 2413-9351 2415-1076 |
| language | English |
| last_indexed | 2024-04-11T14:53:19Z |
| publishDate | 2020-12-01 |
| publisher | Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) |
| record_format | Article |
| series | Jordanian Journal of Computers and Information Technology |
| spelling | doaj.art-e586b8f2c47c4dd59e3eb59ddbeccf582022-12-22T04:17:21ZengScientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)Jordanian Journal of Computers and Information Technology2413-93512415-10762020-12-016434536010.5455/jjcit.71-1590557276IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTINGElham Darbanian0Dadmehr Rahbari1Roghayeh Ghanizadeh2Mohsen Nickray3Department of Computer Engineering and Information Technology, University of Qom, Alghadir Ave., Qom, Iran.Department of Computer Engineering and Information Technology, University of Qom, Alghadir Ave., Qom, Iran.Department of Computer Engineering and Information Technology, University of Qom, Alghadir Ave., Qom, Iran.Department of Computer Engineering and Information Technology, University of Qom, Alghadir Ave., Qom, Iran.The application of computing resources through mobile devices (MDs) is called Mobile Computing; between cloud datacentres and devices, it is known as (Mobile) Fog Computing (MFC). We ran Cloudsim simulator to offload tasks in suitable Fog Devices (FDs), cloud or mobile. We stored the outputs of the simulator as a dataset with features and a target class. A target class is a device in which tasks are offloaded and features of tasks are authentication, confidentiality, integrity, availability, capacity, speed and cost. Decision Tree (DT), Random Forest (RF), Extra-trees and AdaBoost classifiers were classified based on attribute values and the plot of trees was drawn. According to the plot of these classifiers, we extracted each sequential condition from root to leaves and inserted it into the simulator. What these classifiers do is to improve the conditions that should be inserted in the corresponding section of the simulator. We improved the response time of offloading by Random Forest, Extra-trees and AdaBoost over Decision Tree.https://jjcit.org/paper/112fog computingdecision tree classifierrandom forest classifierextra-trees classifieradaboost classifieroffloadingmachine learning |
| spellingShingle | Elham Darbanian Dadmehr Rahbari Roghayeh Ghanizadeh Mohsen Nickray IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING Jordanian Journal of Computers and Information Technology fog computing decision tree classifier random forest classifier extra-trees classifier adaboost classifier offloading machine learning |
| title | IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING |
| title_full | IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING |
| title_fullStr | IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING |
| title_full_unstemmed | IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING |
| title_short | IMPROVING RESPONSE TIME OF TASK AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING |
| title_sort | improving response time of task and adaboost classifiers in mobile fog computing |
| topic | fog computing decision tree classifier random forest classifier extra-trees classifier adaboost classifier offloading machine learning |
| url | https://jjcit.org/paper/112 |
| work_keys_str_mv | AT elhamdarbanian improvingresponsetimeoftaskandadaboostclassifiersinmobilefogcomputing AT dadmehrrahbari improvingresponsetimeoftaskandadaboostclassifiersinmobilefogcomputing AT roghayehghanizadeh improvingresponsetimeoftaskandadaboostclassifiersinmobilefogcomputing AT mohsennickray improvingresponsetimeoftaskandadaboostclassifiersinmobilefogcomputing |