Triggerflow: Trigger-based Orchestration of Serverless Workflows

TL;DR

Triggerflow is an extensible, trigger-based serverless orchestration system that efficiently manages diverse workflows, supports high-volume event processing, auto-scales, and ensures fault tolerance for scientific and data center applications.

Contribution

It introduces Triggerflow, a novel open, trigger-based architecture enabling flexible, efficient, and extensible orchestration of serverless workflows with high scalability and fault tolerance.

Findings

Supports various reactive orchestrators like State Machines and DAGs

Handles high-volume event workloads with auto-scaling and zero-downscale

Ensures fault tolerance and resource efficiency for long-running workflows

Abstract

As more applications are being moved to the Cloud thanks to serverless computing, it is increasingly necessary to support the native life cycle execution of those applications in the data center. But existing cloud orchestration systems either focus on short-running workflows (like IBM Composer or Amazon Step Functions Express Workflows) or impose considerable overheads for synchronizing massively parallel jobs (Azure Durable Functions, Amazon Step Functions). None of them are open systems enabling extensible interception and optimization of custom workflows. We present Triggerflow: an extensible Trigger-based Orchestration architecture for serverless workflows. We demonstrate that Triggerflow is a novel serverless building block capable of constructing different reactive orchestrators (State Machines, Directed Acyclic Graphs, Workflow as code, Federated Learning orchestrator). We also validate that it can support high-volume event processing workloads, auto-scale on demand with scale down to zero when not used, and transparently guarantee fault tolerance and efficient resource usage when orchestrating long running scientific workflows.