Forecasting Hardware Failures or Resource Bottlenecks Before They Occur
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I2P111Keywords:
Predictive Maintenance, Hardware Failure, Resource Bottlenecks, Machine Learning, Anomaly Detection, System MonitoringAbstract
Hardware failures and resources bottlenecks are unpredictable, and this is crucial in determining high system availability, reliability and performance. The predictive analytics based on advanced machine learning algorithms and history of the system of the past will be introduced in this paper as the key to tropical predictions of the errors in the hardware and excessive consumption of resources. System logs, performance power tools and anomaly detectors will help us locate any trends that may warn us about imminent failures. As our methodology, we will use pre-processing of our data, feature discrimination, model training, and real-time monitoring, which will translate into predictive models that will provide warnings to the administrators on critical issues, even before they manifest. The findings confirm that proactive prediction can ensure a considerable decrease in the downtime and improve the use of resources and operational costs. The study has helped in the study of predictive maintenance and anticipatory resource management as it provides a systematized process of failure prediction in various computing systems
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