Proactive Hybrid Autoscaling for Container-Based Edge Applications in Kubernetes

As the rising of the Internet of Things (IoT), edge computing is widely adopted in numerous applications. However, current autoscaling tools are not designed for edge applications and can not utilize the heterogeneous resources of edge nodes efficiently. In this paper, we propose a proactive hybrid autoscaler specifically optimized for edge computing scenario. With the Bidirectional Long Short Term Memory (Bi-LSTM) based load prediction model, the proposed autoscaler is able to predict the future workload and perform scaling operation before it arrives. In addition, a overload compensation algorithm is implemented to mitigate the Quality of Service (QoS) decreasing due to under-prediction. Then, a hybrid scaling method is applied to simultaneously modify the number of pods and their resource quotas without restarting. Experimental results with a real-world workload dataset shows the proposed load prediction model has better accuracy compared with the Long Short Term Memory model and the state-of-the-art statistical analysis model, Autoregressive Integrated Moving Average (ARIMA), which is also more than 350 times slower than our model in prediction speed. Finally, evaluation in a real Kubernetes cluster shows that the proposed proactive hybrid autoscaler outperforms the default Horizontal Pod Autoscaler (HPA) of Kubernetes in terms of both QoS and resource utilization efficiency.