Fog-cloud scheduling simulator for reinforcement learning algorithms
Fog computing is a popular choice for Internet of Things (IoT) applications, such as electricity, health, transportation, smart cities, security, and more. Due to its decentralized architecture, fog computing offers low latency processing and ensures the preservation of information between the sourc...
| Main Authors: | , , , |
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| Format: | Article |
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Springer
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108029/ |
| _version_ | 1848865054515003392 |
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| author | Al-Hashimi, Mustafa Ahmed Adnan Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati |
| author_facet | Al-Hashimi, Mustafa Ahmed Adnan Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati |
| author_sort | Al-Hashimi, Mustafa Ahmed Adnan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Fog computing is a popular choice for Internet of Things (IoT) applications, such as electricity, health, transportation, smart cities, security, and more. Due to its decentralized architecture, fog computing offers low latency processing and ensures the preservation of information between the source and the cloud resources. Additionally, it can be integrated with the cloud to provide satisfactory and efficient service simultaneously. However, the main challenge with fog computing is that the edge nodes, called fog devices, have limited processing capabilities and storage for dynamic high-level operations. Therefore, supplying optimized scheduling algorithms to provide satisfactory quality service for the node’s task execution and processing becomes demanding. Most existing simulators are built based on simplified situations, which causes degradation in performance when realistic scenarios are considered. This study presents a developed simulator that captures all mentioned realistic scenarios by providing the feature of integrability with the reinforcement learning (RL) algorithm. Furthermore, three validation steps have been used to measure the simulator’s effectiveness: real-time visualization, intense task arrival, and preservation test have been used, and the results proved the simulator suitable for dealing with realistic situations. |
| first_indexed | 2025-11-15T13:58:36Z |
| format | Article |
| id | upm-108029 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:58:36Z |
| publishDate | 2023 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1080292024-09-26T04:01:57Z http://psasir.upm.edu.my/id/eprint/108029/ Fog-cloud scheduling simulator for reinforcement learning algorithms Al-Hashimi, Mustafa Ahmed Adnan Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati Fog computing is a popular choice for Internet of Things (IoT) applications, such as electricity, health, transportation, smart cities, security, and more. Due to its decentralized architecture, fog computing offers low latency processing and ensures the preservation of information between the source and the cloud resources. Additionally, it can be integrated with the cloud to provide satisfactory and efficient service simultaneously. However, the main challenge with fog computing is that the edge nodes, called fog devices, have limited processing capabilities and storage for dynamic high-level operations. Therefore, supplying optimized scheduling algorithms to provide satisfactory quality service for the node’s task execution and processing becomes demanding. Most existing simulators are built based on simplified situations, which causes degradation in performance when realistic scenarios are considered. This study presents a developed simulator that captures all mentioned realistic scenarios by providing the feature of integrability with the reinforcement learning (RL) algorithm. Furthermore, three validation steps have been used to measure the simulator’s effectiveness: real-time visualization, intense task arrival, and preservation test have been used, and the results proved the simulator suitable for dealing with realistic situations. Springer 2023 Article PeerReviewed Al-Hashimi, Mustafa Ahmed Adnan and Rahiman, Amir Rizaan and Muhammed, Abdullah and Hamid, Nor Asilah Wati (2023) Fog-cloud scheduling simulator for reinforcement learning algorithms. International Journal of Information Technology (Singapore). ISSN 2511-2104; ESSN: 2511-2112 https://link.springer.com/article/10.1007/s41870-023-01479-1?error=cookies_not_supported&code=90ee88c7-2096-44ad-a4cc-ab5fca21f9a6 10.1007/s41870-023-01479-1 |
| spellingShingle | Al-Hashimi, Mustafa Ahmed Adnan Rahiman, Amir Rizaan Muhammed, Abdullah Hamid, Nor Asilah Wati Fog-cloud scheduling simulator for reinforcement learning algorithms |
| title | Fog-cloud scheduling simulator for reinforcement learning algorithms |
| title_full | Fog-cloud scheduling simulator for reinforcement learning algorithms |
| title_fullStr | Fog-cloud scheduling simulator for reinforcement learning algorithms |
| title_full_unstemmed | Fog-cloud scheduling simulator for reinforcement learning algorithms |
| title_short | Fog-cloud scheduling simulator for reinforcement learning algorithms |
| title_sort | fog-cloud scheduling simulator for reinforcement learning algorithms |
| url | http://psasir.upm.edu.my/id/eprint/108029/ http://psasir.upm.edu.my/id/eprint/108029/ http://psasir.upm.edu.my/id/eprint/108029/ |