StepConf: SLO-Aware Dynamic Resource Configuration for Serverless Function Workflows
Zhaojie Wen, Yishuo Wang, Fangming Liu
Abstract
Function-as-a-Service (FaaS) offers a fine-grained resource provision model, enabling developers to build highly elastic cloud applications. User requests are handled by a series of serverless functions step by step, which forms a function-based workflow. The developers are required to set proper resource configuration for functions, so as to meet service level objectives (SLOs) and save cost. However, developing the resource configuration strategy is challenging. It is mainly because execution of cloud functions often suffers from cold start and performance fluctuation, which requires a dynamic configuration strategy to guarantee the SLOs. In this paper, we present StepConf, a framework that automates the resource configuration for functions as the workflow runs. StepConf optimizes memory size for each function step in the workflow and takes inter and intra-function parallelism into consideration. We evaluate StepConf on AWS Lambda. Compared with baselines, the experimental results show that StepConf can save cost up to 40.9% while ensuring the SLOs.