Trust your local scaler: A continuous, decentralized approach to autoscaling

Autoscaling is a critical component of modern cloud computing environments, improving flexibility, efficiency, and cost-effectiveness. Current approaches use centralized autoscalers that make decisions based on averaged monitoring data of managed service instances in fixed intervals. In this scheme, autoscalers are single points of failure, tightly coupled to monitoring systems, and limited in reaction times, making non-optimal scaling decisions costly. This paper presents an approach for continuous decentralized autoscaling, where decisions are made on a service instance level. By distributing scaling decisions of instances over time, autoscaling evolves into a quasi-continuous process, enabling great adaptability to different workload patterns. We analyze our approach on different abstraction levels, including a model-based, simulation-based, and real-world evaluation. Proof-of-concept experiments show that our approach is able to scale different applications deployed in containers and virtual machines in realistic environments, yielding better scaling performance compared to established baseline autoscalers, especially in scenarios with highly dynamic workloads. • We present the design of a novel decentralized autoscaling approach that enables an asymptotically continuous scaling process. • We conduct a model- and simulation-based analysis for autoscaling based on CPU utilization that shows the convergence, scalability, robustness, and configurability of our approach. • We use a proof of concept implementation to evaluate decentralized autoscaling with different test applications in realistic test environments.