Orchestrating Data-Centric Workflows

Abstract

When orchestrating data-centric workflows as are commonly found in the sciences, centralised servers can become a bottleneck to the performance of a workflow; output from service invocations are normally transferred via a centralised orchestration engine, when they should be passed directly to where they are needed at the next service in the workflow. To address this performance bottleneck, this paper presents a lightweight hybrid workflow architecture and concrete API, based on a centralised control flow, distributed data flow model. Our architecture maintains the robustness and simplicity of centralised orchestration, but facilitates choreography by allowing services to exchange data directly with one another, reducing data that needs to be transferred through a centralised server. Furthermore our architecture is standards compliment, flexible and is a non-disruptive solution; service definitions do not have to be altered prior to enactment.

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