Analyzing Open-Source Serverless Platforms: Characteristics and Performance

TL;DR

This paper compares four open-source serverless platforms, analyzing their frameworks, event models, and performance differences to guide developers in platform selection and future improvements.

Contribution

It provides a detailed analysis of open-source serverless platforms' frameworks, performance factors, and insights for enhancing auto-scaling and metric collection.

Findings

Identified key differences in platform frameworks and event processing models.

Analyzed performance impacts of service exporting and auto-scaling modes.

Provided insights for future improvements in auto-scaling and metrics.

Abstract

Serverless computing is increasingly popular because of its lower cost and easier deployment. Several cloud service providers (CSPs) offer serverless computing on their public clouds, but it may bring the vendor lock-in risk. To avoid this limitation, many open-source serverless platforms come out to allow developers to freely deploy and manage functions on self-hosted clouds. However, building effective functions requires much expertise and thorough comprehension of platform frameworks and features that affect performance. It is a challenge for a service developer to differentiate and select the appropriate serverless platform for different demands and scenarios. Thus, we elaborate the frameworks and event processing models of four popular open-source serverless platforms and identify their salient idiosyncrasies. We analyze the root causes of performance differences between different service exporting and auto-scaling modes on those platforms. Further, we provide several insights for future work, such as auto-scaling and metric collection.