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research-article

Triggerflow: : Trigger-based orchestration of serverless workflows

Authors:

Pedro García López,

Aleksander Slominski,

Lionel VillardAuthors Info & Claims

Volume 124, Issue C

Pages 215 - 229

https://doi.org/10.1016/j.future.2021.06.004

Published: 01 November 2021 Publication History

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.

Highlights

•

Analysis of current serverless orchestration systems for scientific workflows.

•

Specification of a novel trigger-based architecture for serverless orchestration.

•

Demonstration of how Triggerflow orchestrates DAGs, state machines, workflow as code.

•

Prototype implementation of the serverless auto-scalable architecture on Kubernetes.

•

Evaluation of serverless orchestration performance, fault tolerance and efficiency.

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Cited By

Ghasvari Jahrmoi MAshtiani MBakhshi F(2025)FaasFlows: an approach for reducing vendor lock-in and response time in serverless workflowsThe Journal of Supercomputing10.1007/s11227-024-06706-581:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06706-5
Wang YChen PDou HZhang YYu GHe ZHuang HFilkov VRay BZhou M(2024)FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless WorkflowsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695477(957-969)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695477
Triendl SRistov SBalsamo SKnottenbelt WAbad CShang W(2024)Peeking Behind the Serverless Implementations and Deployments of the Montage WorkflowCompanion of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629527.3651420(196-203)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629527.3651420
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Information & Contributors

Information

Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 124, Issue C

Nov 2021

497 pages

ISSN:0167-739X

Issue’s Table of Contents

The Author(s).

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2021

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Cited By

Ghasvari Jahrmoi MAshtiani MBakhshi F(2025)FaasFlows: an approach for reducing vendor lock-in and response time in serverless workflowsThe Journal of Supercomputing10.1007/s11227-024-06706-581:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06706-5
Wang YChen PDou HZhang YYu GHe ZHuang HFilkov VRay BZhou M(2024)FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless WorkflowsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695477(957-969)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695477
Triendl SRistov SBalsamo SKnottenbelt WAbad CShang W(2024)Peeking Behind the Serverless Implementations and Deployments of the Montage WorkflowCompanion of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629527.3651420(196-203)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629527.3651420
Andrei Da Silva AEnriquez RRattihalli GThurimella VFerreira da Silva RMilojicic D(2024)Enabling HPC Scientific Workflows for ServerlessProceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis10.1109/SCW63240.2024.00022(110-125)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SCW63240.2024.00022
Eizaguirre GSánchez-Artigas M(2024)A Seer knows bestJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104763183:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.jpdc.2023.104763
Ristov SBrandacher SHautz MFelderer MBreu R(2024) CODEFuture Generation Computer Systems10.1016/j.future.2024.06.017160:C(442-456)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.future.2024.06.017
Bharti UGoel AGupta S(2023)ReactiveFnJ: A choreographed model for Fork-Join Workflow in Serverless ComputingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00429-312:1Online publication date: 24-Apr-2023
https://dl.acm.org/doi/10.1186/s13677-023-00429-3
Hautz MRistov SFelderer M(2023)Characterizing AFCL Serverless Scientific Workflows in Federated FaaSProceedings of the 9th International Workshop on Serverless Computing10.1145/3631295.3631397(24-29)Online publication date: 11-Dec-2023
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Aldinucci MBaralis ECardellini VColonnelli IDanelutto MDecherchi SDi Modica GFerrucci LGribaudo MIannone FLapegna MMedic DMuscianisi GRighetti FSciacca ETonellotto NTortonesi MTrunfio PVardanega T(2023)A Systematic Mapping Study of Italian Research on WorkflowsProceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624285(2065-2076)Online publication date: 12-Nov-2023
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Castagna FTrombetta ALandoni MAndreon S(2023)A Serverless Architecture for Efficient and Scalable Monte Carlo Markov Chain ComputationProceedings of the 2023 7th International Conference on Cloud and Big Data Computing10.1145/3616131.3616141(68-73)Online publication date: 17-Aug-2023
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