Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909690446741504 |
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| author | Yu, Minchen Ren, Yinghao Zhao, Jiamu Li, Jiaqi |
| author_facet | Yu, Minchen Ren, Yinghao Zhao, Jiamu Li, Jiaqi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_11929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics Yu, Minchen Ren, Yinghao Zhao, Jiamu Li, Jiaqi Distributed, Parallel, and Cluster Computing 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. |
| title | Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2507.11929 |