A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain
Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor loc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/12/3/84 |
| _version_ | 1827304893252108288 |
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| author | Osama Younis Kamal Jambi Fathy Eassa Lamiaa Elrefaei |
| author_facet | Osama Younis Kamal Jambi Fathy Eassa Lamiaa Elrefaei |
| author_sort | Osama Younis |
| collection | DOAJ |
| description | Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases. |
| first_indexed | 2024-04-24T17:47:57Z |
| format | Article |
| id | doaj.art-1756188a356948f4af54fd312605758a |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-04-24T17:47:57Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-1756188a356948f4af54fd312605758a2024-03-27T14:05:42ZengMDPI AGSystems2079-89542024-03-011238410.3390/systems12030084A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using BlockchainOsama Younis0Kamal Jambi1Fathy Eassa2Lamiaa Elrefaei3Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, EgyptIntelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.https://www.mdpi.com/2079-8954/12/3/84intelligent systemsartificial intelligenceblockchainsoftware architecturecloud computing systems |
| spellingShingle | Osama Younis Kamal Jambi Fathy Eassa Lamiaa Elrefaei A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain Systems intelligent systems artificial intelligence blockchain software architecture cloud computing systems |
| title | A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain |
| title_full | A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain |
| title_fullStr | A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain |
| title_full_unstemmed | A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain |
| title_short | A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain |
| title_sort | proposal for a tokenized intelligent system a prediction for an ai based scheduling secured using blockchain |
| topic | intelligent systems artificial intelligence blockchain software architecture cloud computing systems |
| url | https://www.mdpi.com/2079-8954/12/3/84 |
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