Xanadu: Mitigating cascading cold starts in serverless function chain deployments

Abstract

Organization of tasks as workflows are an essential feature to expand the applicability of the serverless computing framework. Existing serverless platforms are either agnostic to function chains (workflows as a composition of functions) or rely on naive provisioning and management mechanisms of the serverless framework-an example is that they provision resources after the trigger to each function in a workflow arrives thereby forcing a setup latency for each function in the workflow. In this work, we focus on mitigating the cascading cold start problem- the latency overheads in triggering a sequence of serverless functions according to a workflow specification. We first establish the nature and extent of the cascading effects in cold start situations across multiple commercial server platforms and cloud providers. Towards mitigating these cascading overheads, we design and develop several optimizations, that are built into our tool Xanadu. Xanadu offers multiple instantiation options based on the desired runtime isolation requirements and supports function chaining with or without explicit workflow specifications. Xanadu's optimizations to address the cascading cold start problem are built on speculative and just-in-time provisioning of resources. Our evaluation of the Xanadu system reveals almost complete elimination of cascading cold starts at minimal cost overheads, outperforming the available state of the art platforms. For even relatively short workflows, Xanadu reduces platform overheads by almost 18x compared to Knative and 10x compared to Apache Openwhisk.