Optimization of artificial intelligence in localized big data real-time query processing task scheduling algorithm
IntroductionThe development of science and technology has driven rapid changes in the social environment, especially the rise of the big data environment, which has greatly increased the speed at which people obtain information. However, in the process of big data processing, the allocation of infor...
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| Vydané v: | Frontiers in physics Ročník 12 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Frontiers Media S.A
24.10.2024
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| Predmet: | |
| ISSN: | 2296-424X, 2296-424X |
| On-line prístup: | Získať plný text |
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| Shrnutí: | IntroductionThe development of science and technology has driven rapid changes in the social environment, especially the rise of the big data environment, which has greatly increased the speed at which people obtain information. However, in the process of big data processing, the allocation of information resources is often unreasonable, leading to a decrease in efficiency. Therefore, optimizing task scheduling algorithms has become an urgent problem to be solved.MethodsThe study optimized task scheduling algorithms using artificial intelligence (AI) methods. A task scheduling algorithm optimization model was designed using support vector machine (SVM) and K-nearest neighbor (KNN) combined with fuzzy comprehensive evaluation. In this process, the performance differences of different nodes were considered to improve the rationality of resource allocation.Results and DiscussionBy comparing the task processing time before and after optimization with the total cost, the results showed that the optimized model significantly reduced task processing time and total cost. The maximum reduction in task processing time is 2935 milliseconds. In addition, the analysis of query time before and after optimization shows that the query time of the optimized model has also been reduced. The experimental results demonstrate that the proposed optimization model is practical in handling task scheduling problems and provides an effective solution for resource management in big data environments. This research not only improves the efficiency of task processing, but also provides new ideas for optimizing future scheduling algorithms. |
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| ISSN: | 2296-424X 2296-424X |
| DOI: | 10.3389/fphy.2024.1484115 |