Dynamic Multi-level Auto-scaling Rules for Containerized Applications

Container-based cloud applications require sophisticated auto-scaling methods in order to operate under different workload conditions. The choice of an auto-scaling method may significantly affect important service quality parameters, such as response time and resource utilization. Current container orchestration systems such as Kubernetes and cloud providers such as Amazon EC2 employ auto-scaling rules with static thresholds and rely mainly on infrastructure-related monitoring data, such as CPU and memory utilization. This paper presents a new dynamic multi-level (DM) auto-scaling method with dynamically changing thresholds, which uses not only infrastructure, but also application-level monitoring data. The new method is compared with seven existing auto-scaling methods in different synthetic and real-world workload scenarios. Based on experimental results, all eight auto-scaling methods are compared according to the response time and the number of instantiated containers. The results show that the proposed DM method has better overall performance under varied amount of workloads than the other auto-scaling methods. Due to satisfactory results, the proposed DM method is implemented in the SWITCH software engineering system for time-critical cloud applications