Function-as-a-Service (FaaS) has become a central paradigm in serverless cloud computing, yet optimizing FaaS deployments remains challenging. Using function fusion, multiple functions can be combined into a single deployment unit, which can be used to reduce cost and latency of complex serverless applications comprising multiple functions. Even in small-scale applications, the number of possible fusion configurations is vast, making brute-force benchmarking in production both cost- and time-prohibitive. In this paper, we present a system that can analyze every possible fusion setup of complex applications. By emulating the FaaS platform, our system enables local experimentation, eliminating the need to reconfigure the live platform and significantly reducing associated cost and time. We evaluate all fusion configurations across a number of example FaaS applications and resource limits. Our results reveal that, when analyzing cost and latency trade-offs, only a limited set of fusion configurations represent optimal solutions, which are strongly influenced by the specific pricing model in use.

翻译：函数即服务（FaaS）已成为无服务器云计算的核心范式，然而优化FaaS部署仍具挑战性。通过函数融合技术，可将多个函数合并为单一部署单元，从而降低由多函数构成的复杂无服务器应用的成本与延迟。即使在小型应用中，可能的融合配置数量也极为庞大，使得在生产环境中进行暴力基准测试在成本和时间上均不可行。本文提出一种能够分析复杂应用所有可能融合配置的系统。通过模拟FaaS平台，该系统支持本地实验，无需重新配置运行中的平台，并显著降低相关成本与时间消耗。我们在多个示例FaaS应用及资源限制条件下评估了所有融合配置。结果表明：在权衡成本与延迟时，仅有有限数量的融合配置能代表最优解，且这些最优解受特定定价模型的显著影响。