Fault tolerance in distributed systems using deep learning approaches.
Recently, distributed systems have become the backbone of technological development. It serves as the foundation for new trends technologies such as blockchain, the internet of things and others. A distributed system provides fault tolerance and decentralization, where a fault in any component does...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310657 |
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| author | Basem Assiri Abdullah Sheneamer |
| author_facet | Basem Assiri Abdullah Sheneamer |
| author_sort | Basem Assiri |
| collection | DOAJ |
| description | Recently, distributed systems have become the backbone of technological development. It serves as the foundation for new trends technologies such as blockchain, the internet of things and others. A distributed system provides fault tolerance and decentralization, where a fault in any component does not result in a whole system failure. In addition, deep learning model enables processing data to find patterns, which helps in classification, regression, prediction, and clustering. This work employs deep learning to handle faults within distributed systems in three scenarios. Firstly, a faulty processor may not be able to produce the right output. Therefore, deep learning model uses the inputs and outputs of other processors to find patterns and produces the proper output of the faulty processor. Secondly, if a faulty possessor corrupts its inputs as well, then the deep learning model learns from the inputs and the outputs of successful processors and produces the proper output of the faulty processor, even with corrupted inputs. Thirdly, for unrelated data, in which the patterns of the input of the faulty processors differ from the patterns of the inputs of successful ones. In this case, the model is able to discover the new pattern and to be labeled as unknown. In the experiments, we use deep learning models like VGG16, VGG19, AlexNet LSTM and ResNet34, to investigate the performance of the deep learning in the three mentioned scenarios. For unstructured datasets, the accuracy of the models is affected by the size of the faulty data. The accuracy of all models lies between 60% when the size of the faulty data is 90%, and 96%, when the size of the faulty data is 90%. The structured datasets are not significantly affected by the portion of the faulty data and the accuracy reaches 99%. |
| format | Article |
| id | doaj-art-8b4833fc739e4c51a029bf0494e8abb8 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-8b4833fc739e4c51a029bf0494e8abb82025-01-17T05:31:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031065710.1371/journal.pone.0310657Fault tolerance in distributed systems using deep learning approaches.Basem AssiriAbdullah SheneamerRecently, distributed systems have become the backbone of technological development. It serves as the foundation for new trends technologies such as blockchain, the internet of things and others. A distributed system provides fault tolerance and decentralization, where a fault in any component does not result in a whole system failure. In addition, deep learning model enables processing data to find patterns, which helps in classification, regression, prediction, and clustering. This work employs deep learning to handle faults within distributed systems in three scenarios. Firstly, a faulty processor may not be able to produce the right output. Therefore, deep learning model uses the inputs and outputs of other processors to find patterns and produces the proper output of the faulty processor. Secondly, if a faulty possessor corrupts its inputs as well, then the deep learning model learns from the inputs and the outputs of successful processors and produces the proper output of the faulty processor, even with corrupted inputs. Thirdly, for unrelated data, in which the patterns of the input of the faulty processors differ from the patterns of the inputs of successful ones. In this case, the model is able to discover the new pattern and to be labeled as unknown. In the experiments, we use deep learning models like VGG16, VGG19, AlexNet LSTM and ResNet34, to investigate the performance of the deep learning in the three mentioned scenarios. For unstructured datasets, the accuracy of the models is affected by the size of the faulty data. The accuracy of all models lies between 60% when the size of the faulty data is 90%, and 96%, when the size of the faulty data is 90%. The structured datasets are not significantly affected by the portion of the faulty data and the accuracy reaches 99%.https://doi.org/10.1371/journal.pone.0310657 |
| spellingShingle | Basem Assiri Abdullah Sheneamer Fault tolerance in distributed systems using deep learning approaches. PLoS ONE |
| title | Fault tolerance in distributed systems using deep learning approaches. |
| title_full | Fault tolerance in distributed systems using deep learning approaches. |
| title_fullStr | Fault tolerance in distributed systems using deep learning approaches. |
| title_full_unstemmed | Fault tolerance in distributed systems using deep learning approaches. |
| title_short | Fault tolerance in distributed systems using deep learning approaches. |
| title_sort | fault tolerance in distributed systems using deep learning approaches |
| url | https://doi.org/10.1371/journal.pone.0310657 |
| work_keys_str_mv | AT basemassiri faulttoleranceindistributedsystemsusingdeeplearningapproaches AT abdullahsheneamer faulttoleranceindistributedsystemsusingdeeplearningapproaches |