Optimal Deployment of Geographically Distributed Workflow Engines on the Cloud

TL;DR

This paper presents a framework that optimally deploys workflow engines across cloud regions to minimize execution time, significantly improving performance for geographically distributed scientific workflows.

Contribution

It introduces an automated constraint-based framework for optimal deployment of workflow engines on cloud regions, reducing execution time compared to centralized approaches.

Findings

Framework reduces workflow execution time by up to 2.5x.

Experimental results validate the effectiveness of the deployment strategy.

Optimized placement improves performance for scientific workflows.

Abstract

When orchestrating Web service workflows, the geographical placement of the orchestration engine(s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the op- timal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of sci- entific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.