Provuse: Platform-Side Function Fusion for Performance and Efficiency in FaaS Environments

2026-03-06

AI Summary

This work addresses the challenges of high invocation latency, excessive resource consumption, and redundant billing commonly encountered in multi-function composition applications on Function-as-a-Service (FaaS) platforms. The authors propose a platform-level, developer-transparent runtime function fusion mechanism that automatically merges independent function instances by transparently managing function entry points and deployment artifacts, achieving performance optimization without any code modifications. Implemented atop tinyFaaS and Kubernetes, the approach is compatible with mainstream container orchestration frameworks. Experimental results demonstrate that the proposed method reduces average end-to-end latency by 26.33% and decreases memory usage by 53.57%, significantly enhancing both execution efficiency and resource utilization for composed serverless applications.

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Abstract

Function-as-a-Service (FaaS) platforms provide scalable and cost-efficient execution but suffer from increased latency and resource overheads in complex applications comprising multiple functions, particularly due to double billing when functions call each other. This paper presents Provuse, a transparent, platform-side optimization that automatically performs function fusion at runtime for independently deployed functions, thereby eliminating redundant function instances. This approach reduces both cost and latency without requiring users to change any code. Provusetargets provider-managed FaaS platforms that retain control over function entry points and deployment artifacts, enabling transparent, runtime execution consolidation without developer intervention. We provide two implementations for this approach using the tinyFaaS platform as well as Kubernetes, demonstrating compatibility with container orchestration frameworks. An evaluation shows consistent improvements, achieving an average end-to-end latency reduction of 26.33% and a mean RAM usage reduction of 53.57%. These results indicate that automatic function fusion is an effective platform-side strategy for reducing latency and RAM consumption in composed FaaS applications, highlighting the potential of transparent infrastructure-level optimizations in serverless systems.

Problem

• Function-as-a-Service
• latency
• resource overhead
• double billing
• function composition

Innovation

• Function Fusion
• Serverless Computing
• FaaS Optimization
• Platform-Side Optimization
• Runtime Consolidation