Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions

2025-10-01

AI Summary

Serverless computing presents dual complexities in function resource configuration—platform opacity and conflicting resource coupling models (e.g., commercial providers linearly scale CPU/bandwidth with memory) versus decoupled resource allocation in open-source frameworks—making it challenging for developers to simultaneously satisfy performance constraints and cost efficiency. Method: We systematically analyze key configuration factors affecting performance and cost in FaaS environments, conduct a comprehensive literature review, and comparatively examine configuration mechanisms across major cloud platforms (AWS Lambda, Azure Functions) and open-source frameworks. Contribution/Results: We propose the first multidimensional taxonomy for function resource configuration, uncovering critical research gaps in dynamic optimization, cross-platform adaptability, and independent resource control. Our structured classification model identifies automated configuration, fine-grained performance prediction, and joint cost-performance optimization as essential future research directions.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its successful execution. This resource allocation influences both the operational expense and the performance quality of these functions. However, a noticeable lack of platform transparency forces developers to rely on expert knowledge or experience-based ad-hoc decisions to request desired function resources. This makes optimal resource configuration a non-trivial task while adhering to performance constraints. Furthermore, while commercial platforms often scale resources like CPU and network bandwidth proportional to memory, open-source frameworks permit independent configuration of function resources, introducing additional complexity for developers aiming to optimise their functions. These complexities have directed researchers to resolve developer challenges and advance towards an efficient server-less execution model. In this article, we identify different aspects of resource configuration techniques in FaaS settings and propose a taxonomy of factors that influence function design, configuration, run-time cost, and performance guarantees. We conduct an analysis of existing literature on resource configuration to present a comprehensive review of current studies on function configuration. We also identify existing research gaps and suggest future research directions to enhance function configuration and strengthen the capabilities of serverless computing environments to drive its broader adoption.

Problem

• Optimizing resource allocation for serverless functions under performance constraints
• Addressing platform opacity in function configuration decisions for developers
• Managing independent resource scaling complexities in open-source serverless frameworks

Innovation

• Dynamic function configuration management in serverless computing
• Taxonomy of factors influencing function design and performance
• Analysis of resource configuration techniques for optimization