  Autoscaling is a critical component for efficient resource utilization with
satisfactory quality of service (QoS) in cloud computing. This paper
investigates proactive autoscaling for widely-used scaling-per-query
applications where scaling is required for each query, such as container
registry and function-as-a-service (FaaS). In these scenarios, the workload
often exhibits high uncertainty with complex temporal patterns like
periodicity, noises and outliers. Conservative strategies that scale out
unnecessarily many instances lead to high resource costs whereas aggressive
strategies may result in poor QoS. We present RobustScaler to achieve superior
trade-off between cost and QoS. Specifically, we design a novel autoscaling
framework based on non-homogeneous Poisson processes (NHPP) modeling and
stochastically constrained optimization. Furthermore, we develop a specialized
alternating direction method of multipliers (ADMM) to efficiently train the
NHPP model, and rigorously prove the QoS guarantees delivered by our
optimization-based proactive strategies. Extensive experiments show that
RobustScaler outperforms common baseline autoscaling strategies in various
real-world traces, with large margins for complex workload patterns.

Distributed, Parallel, and Cluster Computing