Lightweight Vertical Autoscaling Method Using Taylor Series for Serverless Computing

Hyeon-Jun Jang, Hyun-Wook Jin

http://doi.org/10.5626/JOK.2025.52.3.181

Serverless computing has become essential in modern IT infrastructure by utilizing autoscaling to reduce server management burdens, enabling developers to concentrate on service. However, as serverless environments now handle multiple requests per instance, the limitations of horizontal autoscaling have become more apparent. This underscores the increasing need for vertical autoscaling, which dynamically adjusts the resource allocations for each instance. Traditional vertical autoscaling methods, designed for long-running cloud applications, are not well-suited for serverless environments that require rapid response and short execution times. This paper introduces a lightweight vertical autoscaling method that employs Taylor series to enhance both resource efficiency and performance. Experiments with FunctionBench demonstrate that the proposed method reduces resource reservations and wasted resource slack compared to Vertical Pod Autoscaler (VPA) and Tiny Autoscaler, while also improving average and 99th-percentile tail latency. Specifically, when compared to VPA, resource reservations and slack decreased by 18.6% and 45%, respectively, while average and tail latency improved by 31.5% and 53.8%. Additionally, it exhibited the lowest overhead, confirming its effectiveness as a lightweight autoscaling solution.