Styx: An Efficient Workflow Engine for Serverless Platforms

Abstract

Serverless platforms are widely adopted for deploying applications due to their autoscaling capabilities and pay-as-you-go billing models. These platforms execute an application’s functions inside ephemeral containers and scale the number of containers based on incoming request rates. To meet service level objectives (SLOs), they often over-provision resources by maintaining warm containers or rapidly spawning new ones during traffic bursts. However, this strategy frequently leads to inefficient resource utilization, especially during periods of low activity. Prior research addresses this issue through intelligent scheduling, lightweight virtualization, and container-sharing mechanisms. More recent work aims to improve resource utilization by remodeling the execution of a function within a container to better separate compute and I/O stages. Despite these improvements, existing approaches often introduce delays during execution and induce memory pressure under traffic bursts. In this paper, we present Styx, a novel workflow engine that enhances resource utilization by intelligently decoupling compute and I/O stages. Styx employs a fetch latency predictor that uses real-time system metrics from both the serverless node and the remote storage server to accurately estimate prefetch operations, ensuring input data is available exactly when needed. Furthermore, it offloads the output data upload operation from a container to a host-side data service, thereby efficiently managing provisioned memory. Our approach improves the overall memory allocation by 32.6% when running all the serverless workflows simultaneously when compared to Dataflower + Truffle. Additionally, this method improves the tail latency and the mean latency of a workflow by an average of 26.3% and 21%, respectively.