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

2025/07/16 by Minchen Yu, Yu, Minchen, Ren, Yinghao +3
Computer Science · #Blockchain Technology Applications and Security #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2507.11929

openalex publication_date 2025/07/16 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

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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