Predicting and Avoiding SLA Violations of Containerized Applications using Machine Learning and Elasticity

Container-based virtualization represents a low-overhead and easy-to-manage alternative to virtual machines. On the other hand, containers are more prone to performance interference and unpredictability. Consequently, there is growing interest in predicting and avoiding performance issues in containerized environments. Existing solutions tackle this challenge through proactive elasticity mechanisms based on workload variation predictions. Although this approach may yield satisfactory results in some scenarios, external factors such as resource contention can cause performance losses regardless of workload variations. This paper presents Flavor, a machine-learning-based system for predicting and avoiding performance issues in containerized applications. Rather than relying on workload variation prediction as existing approaches, Flavor predicts application-level metrics (e.g., query latency and throughput) through a deep neural network implemented using Tensorflow and scales applications accordingly. We evaluate Flavor by comparing it against a state-of-the-art resource scaling approach that relies solely on workload prediction. Our results show that Flavor can predict performance deviations effectively while assisting operators to wisely scale their services by increasing/decreasing the number of application containers to avoid performance issues and resource underutilization.