Techniques for efficient resource allocation transitions in container cluster autoscaling

Container clusters are widely used to run containerized applications in cloud environments. A key feature of these clusters is autoscaling, the capability to automatically adapt computing resources in response to changing workloads. Autoscaling requires determining resource allocations for successive deployment windows. One strategy to address autoscaling is to use resource allocation algorithms that compute a new allocation for each deployment window based on workload information, but independently of previous allocations. We refer to this approach as history-unaware. A crucial aspect of history-unaware autoscaling is the transition between successive deployment windows, that is, the process of shifting the cluster from one allocation to the next. This transition is particularly challenging because the independence of allocations may result in substantial differences between them. In this paper, we present the Recycling-Based Transition (RBT) algorithm to tackle this challenge. The RBT algorithm introduces a method to identify suitable sets of containers and nodes to preserve across deployment windows, and then orchestrates the required operations to complete the transition. It provides the necessary complement to previous resource allocation approaches that did not address transitions, thereby establishing the foundation of a complete history-unaware autoscaling strategy. To evaluate the suitability of this strategy, we developed a basic simulator that implements history-unaware autoscaling as well as other strategies, including the horizontal autoscaling used in industry-standard systems. Experimental results show promising outcomes: history-unaware autoscaling achieves cost reductions in cluster deployments compared with industry-standard horizontal autoscaling. The impact of the RBT algorithm on cost savings, compared to a baseline algorithm, is also presented in the experimental section.