METHOD FOR WORKLOAD-BASED SELECTION OF LIGHTWEIGHT PREDICTION MODELS IN MICROSERVICE AUTOSCALING
The paper investigates the feasibility of using lightweight predictive models for proactive microservice autoscaling, addressing the limitations of the alternative approaches: reactive threshold-based scaling, causing potential delays in resource adjustment and response time, and the deep learning models, such as LSTM, requiring high computational resource allocation. Using Alibaba Cluster Trace dataset, microservice workloads are analyzed and classified into four distinct categories (Stable, Periodic, Spiky, Mixed) based on the coefficient of variation and peak-to-mean ratio.