Proactive container auto-scaling for cloud native machine learning services

Abstract

Understanding the resource usage behaviors of the ever-increasing machine learning workloads are critical to cloud providers offering Machine Learning (ML) services. Capable of auto-scaling resources for customer workloads can significantly improve resource utilization, thus greatly reducing the cost. Here we leverage the AI4DL framework [1] to characterize workload and discover resource consumption phases. We advance the existing technology to an incremental phase discovery method that applies to more general types of ML workload for both training and inference. We use a time-window MultiLayer Perceptron (MLP) to predict phases in containers with different types of workload. Then, we propose a predictive vertical auto-scaling policy to resize the container dynamically according to phase predictions. We evaluate our predictive auto-scaling policies on 561 long-running containers with multiple types of ML workloads. The predictive policy can reduce up to 38% of allocated CPU compared to the default resource provisioning policies by developers. By comparing our predictive policies with commonly used reactive auto-scaling policies, we find that they can accurately predict sudden phase transitions (with an F1-score of 0.92) and significantly reduce the number of out-of-memory errors (350 vs. 20). Besides, we show that the predictive auto-scaling policy maintains the number of resizing operations close to the best reactive policies.