ChunkFunc: Dynamic SLO-Aware Configuration of Serverless Functions

Abstract

Serverless computing promises to be a cost effective form of on demand computing. To fully utilize its cost saving potential, workflows must be configured with the appropriate amount of resources to meet their response time Service Level Objective (SLO), while keeping costs at a minimum. Since determining and updating these configuration models manually is a nontrivial and error prone task, researchers have developed solutions for automatically finding configurations that meet the aforementioned requirements. However, our initial experiments show that even when following best practices and using state-of-the-art configuration tools, resources may still be considerably over- or underprovisioned, depending on the size of functions’ input payload. In this paper we present ChunkFunc, an SLO- and input data-aware framework for tuning serverless workflows. Our main contributions include: i) an SLO- and input size-aware function performance model for optimized configurations in serverless workflows, ii) ChunkFunc Profiler, an auto-tuned, Bayesian Optimization-guided profiling mechanism for profiling serverless functions with typical input data sizes to build a performance model, and iii) ChunkFunc Workflow Optimizer, which uses these models to determine an input size dependent configuration for each serverless function in a workflow to meet the SLO, while keeping costs to a minimum. We evaluate ChunkFunc on real-life serverless workflows and compare it to two state-of-the-art solutions, showing that it increases SLO adherence by a factor of 1.04 to 2.78, depending on the workflow, and reduces costs by up to 61% .