Adaptive Feature Selection for Predicting Application Performance Degradation in Edge Cloud Environments

Abstract

Applications deployed in edge cloud environments can have stringent requirements such as high throughput and high availability. However, these applications may suffer from performance degradation caused by various underlying reasons such as infrastructure-related faults. Handling application performance degradation proactively is thus critical for maintaining the application Quality-of-Service (QoS). This can be achieved through predicting application performance degradation using Machine Learning (ML) models. The performance of these ML models may degrade over time due to changes in the relevancy of features used for training the ML model for application performance degradation, i.e., feature drift. In this paper, we predict application performance degradation in edge clouds and propose a framework for adapting to the feature drifts that may occur in this environment. This framework detects a feature drift using performance of the prediction model as well as feature importance, and updates the features and adapts the prediction model to the drift considering the severity of the feature drift. We have built a proof-of-concept of our proposed framework on a Kubernetes testbed. It is demonstrated that the proposed framework can achieve up to 9.1% higher F1-score compared to Dynamic Correlation-based Feature Selection (DCFS) approach for feature drift adaptation from the literature.