Decomposing and Executing Serverless Applications as Resource Graphs

Today's serverless computing has several key limitations including per-function resource limits, fixed CPU-to-memory ratio, and constant resource allocation throughout a function execution and across different invocations of it. The root cause of these limitations is the "function-centric" model: a function is a fixed-size box that is allocated, executed, and terminated as an inseparable unit. This unit is pre-defined by the cloud provider and cannot properly capture user needs. We propose a "resource-centric" model for serverless computing that captures fine-grained resource needs throughout an application's execution using components of distinct resource type, amount, and time span. We build a new resource-based serverless execution platform that executes components in a disaggregated, on-demand, and auto-scaled manner. Our results show that ReSC solves various resource-related issues of today's serverless computing, while retaining or even improving its performance.