Auto-scaling mechanisms in serverless computing: A comprehensive review

Abstract

The auto-scaling feature is fundamental to serverless computing, and it automatically allows applications to scale as needed. Hence, this allows applications to be configured to adapt to current traffic and demands and acquire resources as necessary without the need to manage servers directly. Auto-scaling is an important principle in developing serverless applications that is considered and increasingly recognized by academia and industry. Despite the strong interest in auto-scaling in serverless computing in the scientific and industrial community, no clear, comprehensive, and systematic investigation has been conducted. As part of the study of automatic scaling in serverless computing, key strategies and

approaches are investigated during the lifecycle of cloud applications. This research examines three key approaches to automatically scaling serverless computing applications in the taxonomy presented. These approaches include machine learning (ML)-based, frameworks-based, and models-based. Additionally, we provide an overview of key performance metrics essential to the auto-scaling process of cloud applications and discuss the requirements. It discusses key concepts and limitations of serverless computing approaches, challenges, future directions, and research opportunities.