Triggerflow: Trigger-based Orchestration of Serverless Workflows

TL;DR

Triggerflow is an extensible, trigger-based orchestration system for serverless workflows that supports various reactive schedulers, high-volume event processing, auto-scaling, and workflow optimization on cloud platforms.

Contribution

It introduces Triggerflow, a novel open system architecture built on Knative and Kubernetes for flexible, efficient serverless workflow orchestration.

Findings

Supports construction of diverse reactive schedulers

Handles high-volume event workloads effectively

Enables auto-scaling and workflow optimization

Abstract

As more applications are being moved to the Cloud thanks to serverless computing, it is increasingly necessary to support native life cycle execution of those applications in the data center. But existing systems either focus on short-running workflows (like IBM Composer or Amazon Express Workflows) or impose considerable overheads for synchronizing massively parallel jobs (Azure Durable Functions, Amazon Step Functions, Google Cloud Composer). 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 built on top of Knative Eventing and Kubernetes technologies. We demonstrate that Triggerflow is a novel serverless building block capable of constructing different reactive schedulers (State Machines, Directed Acyclic Graphs, Workflow as code). We also validate that it can support high-volume event processing workloads, auto-scale on demand and transparently optimize scientific workflows.