PROACTIVELY ACCOMODATING PREDICTED FUTURE SERVERLESS WORKLOADS USING A MACHINE LEARNING PREDICTION MODEL

摘要

Example implementations relate to a proactive auto-scaling approach. According to an example, a machine-learning prediction model is trained to forecast future serverless workloads during a window of time for an application running in a public cloud based on past serverless workload information associated with the application by performing a training process. During the window of time, serverless workload information associated with the application is monitored. A future serverless workload is predicted for the application at a future time within the window, based on the machine learning prediction model. Prior to the future time, containers within the public cloud executing the application are pre-warmed to accommodate the predicted future serverless workload by issuing fake requests to the application to trigger auto-scaling functionality implemented by the public cloud.