Reinforcement R-learning model for time scheduling of on-demand fog placement
On the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. Nevertheless, taking a real-life scenario, the cloud might limit the number of fogs to place for minimizing the complexity of monitoring a large number of fogs and cost for volunt...
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| Format: | article |
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2020
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| Online Access: | http://hdl.handle.net/10725/12676 https://doi.org/10.1007/s11227-019-03032-z http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/article/10.1007/s11227-019-03032-z |
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| _version_ | 1864513490116411392 |
|---|---|
| author | Farhat, Peter |
| author2 | Sami, Hani Mourad, Azzam |
| author2_role | author author |
| author_facet | Farhat, Peter Sami, Hani Mourad, Azzam |
| author_role | author |
| dc.creator.none.fl_str_mv | Farhat, Peter Sami, Hani Mourad, Azzam |
| dc.date.none.fl_str_mv | 2020 2021-04-08T14:34:47Z 2021-04-08T14:34:47Z 2021-04-08 |
| dc.identifier.none.fl_str_mv | 0920-8542 http://hdl.handle.net/10725/12676 https://doi.org/10.1007/s11227-019-03032-z Farhat, P., Sami, H., & Mourad, A. (2020). Reinforcement R-learning model for time scheduling of on-demand fog placement. The Journal of Supercomputing, 76(1), 388-410. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/article/10.1007/s11227-019-03032-z |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Journal of Supercomputing |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | On the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. Nevertheless, taking a real-life scenario, the cloud might limit the number of fogs to place for minimizing the complexity of monitoring a large number of fogs and cost for volunteers that do not offer their resources for free. This implies choosing the right time and best volunteer to create a fog which the cloud can benefit from is essential. This choice is subject to study the demand of a particular location for services in order to maximize the resources utilization of these fogs. A simple algorithm will not be able to explore randomly changing users’ demands. Therefore, there is a need for an intelligent model capable of scheduling fog placement based on the user’s requests. In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service requesters and produces a suitable fog placement schedule based on the concept of average reward. Our model aims to decrease the cloud’s load by utilizing the maximum available fogs resources over different locations. An implementation of our proposed R-learning model is provided in the paper, followed by a series of experiments on a real dataset to prove its efficiency in utilizing fog resources and minimizing the cloud’s load. We also demonstrate the ability of our model to improve over time by adapting the new demand of users. Experiments comparing the decisions of our model with two other potential fog placement approaches used for task/service scheduling (threshold based and random based) show that the number of processed requests performed by the cloud decreases from 100 to 30% with a limited number of fogs to push. These results demonstrate that our proposed Fog Scheduling Decision model plays a crucial role in the placement of the on-demand fog to the right location at the right time while taking into account the user’s needs. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_fc7b9fe61dd36b01e83d090f0baff984 |
| identifier_str_mv | 0920-8542 Farhat, P., Sami, H., & Mourad, A. (2020). Reinforcement R-learning model for time scheduling of on-demand fog placement. The Journal of Supercomputing, 76(1), 388-410. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/12676 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Reinforcement R-learning model for time scheduling of on-demand fog placementFarhat, PeterSami, HaniMourad, AzzamOn the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. Nevertheless, taking a real-life scenario, the cloud might limit the number of fogs to place for minimizing the complexity of monitoring a large number of fogs and cost for volunteers that do not offer their resources for free. This implies choosing the right time and best volunteer to create a fog which the cloud can benefit from is essential. This choice is subject to study the demand of a particular location for services in order to maximize the resources utilization of these fogs. A simple algorithm will not be able to explore randomly changing users’ demands. Therefore, there is a need for an intelligent model capable of scheduling fog placement based on the user’s requests. In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service requesters and produces a suitable fog placement schedule based on the concept of average reward. Our model aims to decrease the cloud’s load by utilizing the maximum available fogs resources over different locations. An implementation of our proposed R-learning model is provided in the paper, followed by a series of experiments on a real dataset to prove its efficiency in utilizing fog resources and minimizing the cloud’s load. We also demonstrate the ability of our model to improve over time by adapting the new demand of users. Experiments comparing the decisions of our model with two other potential fog placement approaches used for task/service scheduling (threshold based and random based) show that the number of processed requests performed by the cloud decreases from 100 to 30% with a limited number of fogs to push. These results demonstrate that our proposed Fog Scheduling Decision model plays a crucial role in the placement of the on-demand fog to the right location at the right time while taking into account the user’s needs.Published2021-04-08T14:34:47Z2021-04-08T14:34:47Z20202021-04-08Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0920-8542http://hdl.handle.net/10725/12676https://doi.org/10.1007/s11227-019-03032-zFarhat, P., Sami, H., & Mourad, A. (2020). Reinforcement R-learning model for time scheduling of on-demand fog placement. The Journal of Supercomputing, 76(1), 388-410.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://link.springer.com/article/10.1007/s11227-019-03032-zenJournal of Supercomputinginfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/126762021-04-08T14:35:26Z |
| spellingShingle | Reinforcement R-learning model for time scheduling of on-demand fog placement Farhat, Peter |
| status_str | publishedVersion |
| title | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| title_full | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| title_fullStr | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| title_full_unstemmed | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| title_short | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| title_sort | Reinforcement R-learning model for time scheduling of on-demand fog placement |
| url | http://hdl.handle.net/10725/12676 https://doi.org/10.1007/s11227-019-03032-z http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/article/10.1007/s11227-019-03032-z |