Auto-adaptive Fault Prediction System for Edge Cloud Environments in the Presence of Concept Drift

Abstract

6G networks are envisioned to be trustworthy systems that can self-maintain their reliability and service availability. The ability to automatically recognize and predict any faults or failures that occur while delivering services is a step towards realizing such systems. Real-time analytics based on Artificial Intelligence (AI)/Machine Learning (ML) techniques provides the required functionality. However, in the highly dynamic and complex environment of 6G services, the distribution of data feeding to the ML models is subject to change over time (i.e., concept drift), which makes persistent model accuracy challenging without any frequent model retraining. To address this issue, in this paper, we propose a system that predicts the faults in an edge cloud environment, where the prediction models are trained and automatically adapted to the concept drifts via Transfer Learning (TL). To present the effectiveness of our system, we implemented an edge cloud testbed and introduced CPU over-utilization and network congestion fault to it in the presence of concept drift. We used Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and a combination of them as fault prediction models. For detecting the drifts, we have implemented and compared four drift detection methods, and we utilized three TL scenarios for adapting the model to the drift. Our results indicate the superiority of our system in maintaining a persistent accuracy in the presence of concept drift compared to a prediction system without a drift handling entity.