Automatic data featurization for enhanced proactive service auto-scaling: Boosting forecasting accuracy and mitigating oscillation

Abstract

Edge computing has gained widespread adoption for time-sensitive applications by offloading a portion of IoT system workloads from the cloud to edge nodes. However, the limited resources of IoT edge devices hinder service deployment, making auto-scaling crucial for improving resource utilization in response to dynamic workloads. Recent solutions aim to make auto-scaling proactive by predicting future workloads and overcoming the limitations of reactive approaches. These proactive solutions often rely on time-series data analysis and machine learning techniques, especially Long Short-Term Memory (LSTM), thanks to its accuracy and prediction speed. However, existing auto-scaling solutions often suffer from oscillation issues, even when using a cooling-down strategy. Consequently, the efficiency of proactive auto-scaling depends on the prediction model accuracy and the degree of oscillation in the scaling actions.This paper proposes a novel approach to improve prediction accuracy and deal with oscillation issues. Our approach involves an automatic featurization phase that extracts features from time-series workload data, improving the prediction’s accuracy. These extracted features also serve as a grid for controlling oscillation in generated scaling actions. Our experimental results demonstrate the effectiveness of our approach in improving prediction accuracy, mitigating oscillation phenomena, and enhancing the overall auto-scaling performance.

Keywords

• Container
• Auto-scaling
• LSTM
• Oscillation mitigation
• Data featurization
• Time-series forecasting