Evaluating Container-based and WebAssembly-based Serverless Platforms

Abstract

Serverless computing, often also referred to as Function-as-a-Service (FaaS), allows de- velopers to write scalable event-driven applications while the cloud provider manages the burden of provisioning and maintaining compute resources. Serverless computing is en- abled using virtualized sandboxes like containers or lightweight virtual machines that form the execution units for FaaS applications. However, applications suffer from expensive startup latency (cold starts) due to the compulsory overhead of creating a sandbox and initializing the application code and its dependencies. FaaS platforms keep function ex- ecutors warm in memory to avoid this latency which incurs additional memory overhead on the system. Recently, WebAssembly (Wasm) has emerged as a promising alternative for FaaS applications with its lightweight sandboxing, providing negligible startup delays and reduced memory footprint. However, Wasm applications experience slower execution speeds compared to native execution. This thesis presents a performance evaluation of WebAssembly-based serverless computing in comparison with container-based serverless platforms using analytical performance models and its experimental evaluation. The per- formance model for container-based serverless platforms is used from existing literature, reflecting the behavior of commercial platforms like AWS Lambda, IBM Cloud Functions, and Azure Functions. For WebAssembly-based serverless platforms, this thesis proposes a new performance model based on queueing systems. These models are verified exper- imentally using open-source platforms: Apache OpenWhisk for containers and Spin for WebAssembly. A suite of representative serverless applications is used to validate the models. The comparison of the performance models with experimental results highlights the trade-offs between container-based and WebAssembly-based serverless platforms, pro- viding insights into their respective efficiencies in handling serverless workloads.