End-to-end latency and cost impact of function segregation and customized memory allocation in FaaS environments

Abstract: Function as a service (FaaS) is a type of serverless cloud computing intended to facilitate development by abstracting away infrastructural management, and offer a more flexible, pay-as-you-go billing model based on execution time and memory allocation. FaaS functions are deployed to the cloud provider as either single units, or chained to form a pipeline of multiple functions that call each other. As each step in the pipeline might have different requirements, it could be beneficial to split larger functions into smaller parts. This would enable customized provisioning according to each function's needs, and potentially result in a lower rate. However, decreased memory entails a lower CPU performance, which directly correlates to computation time. A test application was created and executed on Google Cloud services to investigate what impact function segregation, and provisioning accommodated to each sub-function requirement, have on end-to-end latency and total cost. In conclusion, no trivial relation between cost and performance was found. Segregating and adjusting provisioning to required memory was in this experiment cheaper in some cases, but not all; however, always significantly slower. In addition to price and workload behavior being considered and balanced, it was found that aspects such as level of control over management and hardware configuration has to be weighed in when deciding if FaaS is a suitable alternative for a given situation.