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Autoscaling in Serverless Computing : Taxonomy and OpenChallenges
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DOI：10.21203/rs.3.rs-2897886/v1.png)
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Abstract
The popularity of serverless computing has been fueled by its operational simplicity, pay-per-use pricing model, and the ability to autoscale. However, there is a lack of comprehensive reviews that focus on the autoscaling context in serverless computing. In this paper, we address this gap by proposing a taxonomy of autoscaling properties for serverless computing. To gather relevant information, we review recent contributions on autoscaling in serverless computing from 2018 to 2022. Using the proposed taxonomy, we analyze the existing autoscaling solutions. Our analysis reveals that the scaling objectives explored by researchers are limited to certain elements, and the existing serverless autoscaling approaches do not provide guarantees that the scaling policies or strategies can meet the Service Level Agreement (SLA) requirements. We conclude by recommending open challenges from three perspectives: verification of autoscaling, energy-driven autoscaling, and anomaly-aware autoscaling. These challenges highlight the need for future research to address the limitations of existing autoscaling approaches and provide more robust and reliable autoscaling mechanisms for serverless computing.
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