一句话：提出静态分析框架 Skyler，在部署前预测 Serverless 应用的 API 调用成本并定位成本热点。
Serverless 应用的费用主要由云 API 调用主导，而分层定价模型使成本难以预测，一个被忽视的设计选择就可能带来数千美元的开销。现有工具存在明显不足：云厂商提供的计算器依赖不切实际的人工估算，动态 profiler 又只能在部署之后使用。本文提出 Skyler，一个用于 Serverless 工作流中 API 调用成本预估算的静态分析框架。Skyler 对程序控制流行为与 API 定价语义进行建模，借助 SMT 公式构造符号化成本表达式，从而暴露 economic sink，即 API 使用对总成本影响不成比例的代码路径，帮助开发者早期发现成本热点、避免昂贵的架构错误。该工具支持 AWS Lambda、Google Cloud Functions 与 Azure Functions 上的 JavaScript Serverless 应用，评估显示其预测精度很高：AWS 与 Google 的平均绝对百分比误差低于 1%，Azure 为 4.5%。
Unpredictable costs are a growing concern in serverless computing, where applications rely on cloud APIs with complex tiered pricing models. In many deployments, API calls dominate expenses, and a single overlooked design choice can escalate costs by thousands of dollars. Existing tools fall short: provider calculators need unrealistic manual estimates, and dynamic profilers only work post-deployment. We present Skyler, a static analysis framework for pre-deployment cost estimation of API invocations in serverless workflows. Skyler models control flow behavior and pricing semantics to construct symbolic cost expressions using SMT formulas, exposing economic sinks, i.e., code paths where API usage disproportionately impacts cost. This enables developers to identify hotspots and prevent costly architectural errors early. Skyler supports JavaScript-based serverless applications across AWS Lambda, Google Cloud Functions, and Azure Functions, achieving high accuracy (mean absolute percentage error &#lt;1% for AWS and Google, 4.5% for Azure).
@inproceedings{RibeiroFSB026,
title = {Skyler: Static Analysis for Predicting API-Driven Costs in Serverless Applications},
author = {Bernardo Ribeiro and Mafalda Ferreira and José Fragoso Santos and Rodrigo Bruno and Nuno Santos},
booktitle = {ASPLOS},
year = {2026},
doi = {10.1145/3779212.3790221}
}