With the proliferation of Serverless Computing, the Function-asa-Service (FaaS) paradigm is nowadays ubiquitous. As a result, the domain has attracted extensive research, both in industry and academia, identifying opportunities and addressing limitations across all aspects of this new Cloud paradigm. Recently, FaaS providers have released production workload traces of their commercial platforms. These expose important characteristics, such as the execution time of function invocations, their number and the distribution of their inter-arrival times, which must be taken into account for a concrete evaluation of innovative solutions. Nevertheless, the Serverless ecosystem still lacks a unified evaluation methodology based on such information. In this paper we attempt to fill this gap, by developing a methodology for fitting existing, real, open-source workloads found in FaaS benchmarking suites to production FaaS workload traces, in a way that sufficiently preserves the aforementioned core statistical properties of such traces. Based on this, we build FaaSRail, an opensource load generator that receives a target maximum request rate and a target total execution duration as inputs from the user and generates representative, scaled down FaaS load.