RDOF: Deployment Optimization for Function as a Service

Function as a service (FaaS) simplifies the runtime resource management of cloud applications and enables fine-grained scaling and billing at the function level, thus becoming the most widespread serverless paradigm today. Cost-effective use of FaaS entails appropriately deploying individual functions. We propose in this paper RDOF1, a model-driven approach to deployment optimization for FaaS. RDOF predicts the performance of a FaaS-based application by instantiating a layered queueing network and finds the optimal configuration of each function such that the total operating cost is minimized under the specified performance requirements. We have validated RDOF on Amazon Web Services (AWS) and implemented it in an online tool that operates on TOSCA metamodels.