Artificial Intelligence-Driven Fault Tolerance Mechanisms for Distributed Systems Using Deep Learning Model
Anila Gogineni · Journal of Artificial Intelligence Machine Learning and Data Science · 2023
The foundation of technical advancement recently has been distributed systems.New technological developments, such as the internet of things, have it as their basis.However, permitting the failure of a single component within the system does not bring the whole system down, which is easily achieved through the use of distributed systems.This paper analyzes the use of deep learning models in artificial intelligence (AI) driven fault tolerance mechanisms for fault detection and fault mitigations in distributed systems.Pre-processing techniques like feature extraction and normalization were employed in order to prepare a distracted driver dataset that would be useful to train and test a model.It was further compared with other standard machine learning algorithms like Naïve Bayes (NB) to determine that the application of deep learning produces better results in fault detection.The performance tested showed that the VGG16 model was able to achieve an accuracy of 92%, with a precision of 91.73%, recall of 91.70% and F1-score of 91.70% was higher than the traditional method used in this research.The performance of the classifiers was evaluated in different conditions involving faulty data and it was observed that the classification accuracy decreases with increased amount of missing or unknown data.However, there are still some constraints with the method, these include high computational costs and low numbers of datasets.