Profiling the Energy Consumption of Serverless Functions with Joule Profiler

2026-09-29

AI Summary

This study addresses the energy consumption black box in serverless computing by systematically quantifying the impact of multi-layered execution environments on energy efficiency. We construct a benchmarking platform integrated with multidimensional energy monitoring techniques to comprehensively evaluate the performance of programming languages, runtimes, and underlying infrastructure across 1,401 configuration combinations. Our empirical analysis demonstrates that programming languages and runtimes are the dominant factors governing energy consumption, motivating us to propose a "language-first" configuration optimization strategy. To foster reproducibility and further research, we open-source all experimental datasets and analytical code. This work establishes an empirical foundation and provides practical guidance for advancing green computing in serverless architectures.

Abstract

Cloud providers and customers have widely adopted serverless computing as a convenient paradigm for deploying and executing functions on demand. To do so, serverless platforms require provisioning an appropriate execution environment before a single line of the function's code runs. These environments consist of several layers, such as container engines, hypervisors, unikernels, and programming language runtimes. While the literature has investigated the performance of these serverless platforms, it treats functions as black boxes, and the community lacks key insights into the environmental impacts of packaging applications as serverless functions. This paper therefore empirically studies the energy efficiency of serverless functions deployable on serverless platforms. We design an experimental benchmarking environment that lets stakeholders explore the impacts of the various layers involved in executing serverless functions. We use it to evaluate 1,401 configurations, combining 9 execution environments, 7 language-runtime configurations, 11 workloads, and 3 input sizes, to answer three research questions: Are the most popular programming languages for serverless functions the most energy-efficient? What factors most affect their energy efficiency? What are the most energy-efficient configurations to deploy them? Our results show that one should first choose the programming language, then the language runtime, and only then the execution environment, which matters only for short-lived functions and whose best choice depends on the runtime. Our benchmarking environment, experimental artifacts, raw measurements, and analysis code are publicly available.

Problem

• Serverless Computing
• Energy Efficiency
• Function Profiling
• Green Computing

Innovation

• Serverless Computing
• Energy Profiling
• Benchmarking
• Empirical Study
• Joule Profiler