Deep Learning Techniques for Predicting System Performance Degradation and Proactive Mitigation

Abstract

In the era of complex systems and intricate dependencies, the ability to anticipate and mitigate performance 
degradation is paramount for ensuring smooth operations and minimizing disruptions. Traditional methods 
often fall short in providing timely and accurate predictions, necessitating the exploration of advanced 
techniques such as deep learning. This abstract encapsulates the essence of leveraging deep learning 
methodologies for forecasting system performance degradation and implementing proactive mitigation 
strategies.This research delves into the application of deep learning models, including convolutional neural 
networks (CNNs), recurrent neural networks (RNNs), and their variants, in predicting system performance 
degradation. By harnessing vast datasets comprising historical performance metrics, system logs, and 
environmental factors, these models can discern intricate patterns and correlations indicative of degradation 
onset. Moreover, the utilization of techniques like transfer learning and ensemble methods enhances model 
generalization and robustness across diverse system architectures and operational conditions