Triggerflow: Trigger-based Orchestration of Serverless Workflows
arXiv:2006.08654 · doi:10.1145/3401025.3401731
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.
The 14th ACM International Conference on Distributed and Event-based Systems (DEBS 2020)
References in corpus (9)
- Federated Learning for Mobile Keyboard Prediction
- Towards Federated Learning at Scale: System Design
- Formal Foundations of Serverless Computing
- Occupy the Cloud: Distributed Computing for the 99%
- Comparison of FaaS Orchestration Systems
- Ripple: A Practical Declarative Programming Framework for Serverless Compute
- Serverless Workflows with Durable Functions and Netherite
- Whiz: A Fast and Flexible Data Analytics System
- In Search of a Fast and Efficient Serverless DAG Engine