SimLess: Simulate ServerlessWorkflows and Their Twins and Siblings in Federated FaaS

Abstract

Many researchers migrate scientific serverless workflows or function choreographies (FCs) on Function-as-a-Service (FaaS) to benefit from its high scalability and elasticity. Unfortunately, the heterogeneity of federated FaaS hampers decisions on appropriate parameter setup to run FCs. Consequently, scientists must choose between accurate but tedious and expensive experiments or simple but cheap and less accurate simulations. Unfortunately, related works support either simulation models for serverfull workflows running on virtual machines and containers or partial FaaS models for individual serverless functions focused on execution time and neglecting various kinds of federated overheads. This paper introduces SimLess, an FC simulation framework for accurate FC simulations across multiple FaaS providers with a simple and lightweight parameter setup. Unlike the costly approaches that use machine learning over time series to predict the FC behavior, SimLess introduces two light concepts: (1) twins, representing the same function deployed with the same computing, communication, and storage resources, but in other regions of the same FaaS provider, and (2) siblings, representing the same function deployed in the same region with different computing resources. The novel SimLess FC simulation model splits the round trip time of a function into several parameters reused among twins and siblings without necessarily running them. We evaluated SimLess with two scientific FCs deployed across 18 AWS, Google, and IBM regions. SimLess simulates the cumulative overhead with an average inaccuracy of 8.9 % without significant differences between regions for learning and validation. Moreover, SimLess uses measurements of a low-concurrency FC executed in a single region to simulate a high-concurrency FC with 2,500 functions in the other areas with an inaccuracy of up to 9.75 %. Finally, SimLess reduces the parameter setup effort by 77.23 % compared to other simulation approaches.