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arXiv · 2507.11929

Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics

Minchen Yu·Yinghao Ren·Jiamu Zhao·Jiaqi Li
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Abstract

Serverless computing has attracted a broad range of applications due to its ease of use and resource elasticity. However, developing serverless applications often poses a dilemma -- relying on general-purpose serverless platforms can fall short of delivering satisfactory performance for complex workloads, whereas building application-specific serverless systems undermines the simplicity and generality. In this paper, we propose an extensible design principle for serverless computing. We argue that a platform should enable developers to extend system behaviors for domain-specialized optimizations while retaining a shared, easy-to-use serverless environment. We take data analytics as a representative serverless use case and realize this design principle in Proteus. Proteus introduces a novel abstraction of decision workflows, allowing developers to customize control-plane behaviors for improved application performance. Preliminary results show that Proteus's prototype effectively optimizes analytical query execution and supports fine-grained resource sharing across diverse applications.

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Minchen Yu, Yinghao Ren, Jiamu Zhao, Jiaqi Li. 2025-07-16. Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics. https://arxiv.org/abs/2507.11929

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