Serverless Abstractions for Short-Running, Lightweight Streams

2026-03-03

AI Summary

This work addresses the inefficiency of existing serverless computing and stream processing systems in handling short-lived, lightweight, and unpredictable stateful data streams. To bridge this gap, the paper proposes “stream functions”—an extension to the function-as-a-service model that elevates short streams to first-class units of execution, state management, and autoscaling. Stream functions express inter-event logic through iterator-based interfaces, effectively integrating stream processing semantics with the elasticity of serverless architectures. This approach is the first to treat short streams as fundamental execution units, thereby filling a critical void in lightweight stateful stream processing. Experimental evaluation in video processing scenarios demonstrates that the proposed system reduces runtime overhead by approximately 99% compared to mainstream stream processing engines, while maintaining performance comparable to conventional serverless functions.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Serverless computing and stream processing represent two dominant paradigms for event-driven data processing, yet both make assumptions that render them inefficient for short-running, lightweight, and unpredictable streams that require stateful processing. We propose stream functions as a novel extension of the Function-as-a-Serivce model that treat short streams as the unit of execution, state, and scaling. Stream functions process streams via an iterator-based interface, enabling seamless inter-event logic while retaining the elasticity and scale-to-zero capabilities offered by serverless platforms. Our evaluation shows that stream functions reduce the processing overhead by ~99 % compared to a mature stream process- ing engine in a video-processing use case. By providing comparable performance to serverless functions with stream semantics, stream functions provide an effective and efficient abstractions for a class of workloads underserved by existing models.

Problem

• serverless computing
• stream processing
• short-running streams
• stateful processing
• lightweight streams

Innovation

• stream functions
• serverless computing
• lightweight streams
• stateful processing
• Function-as-a-Service