Optimal Deployment of Geographically Distributed Workflow Engines on the Cloud
Optimal Deployment of Geographically Distributed Workflow Engines on the Cloud
复制标题
DOI：
10.1109/cloudcom.2014.30
复制
发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
Long Thai;A. Barker;B. Varghese;Ozgur Akgun;Ian Miguel
中科院分区：
文献类型：
--
作者：
Long Thai;A. Barker;B. Varghese;Ozgur Akgun;Ian Miguel
在编排Web服务工作流时，编排引擎的地理位置会极大地影响工作流的性能。数据可能必须跨越很长的地理距离进行传输，这反过来又增加了执行时间并降低了工作流的整体性能。在本文中，我们提出了一个框架，在给定基于dag的工作流规范的情况下，计算最佳的Amazon EC2云区域来部署编排引擎并执行工作流。该框架结合了一个约束模型来解决工作流部署问题，该模型是使用自动化约束建模系统生成的。通过执行代表科学工作负载的不同示例工作流来评估框架的可行性。实验结果表明，该框架减少了工作流的执行时间，并提供了1.3 -2.5倍的速度比集中式方法。
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 optimal 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 scientific 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.