Location, Location, Location: Data-Intensive Distributed Computing in the Cloud
Location, Location, Location: Data-Intensive Distributed Computing in the Cloud
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位置，位置，位置：云中的数据密集型分布式计算
DOI：
10.1109/cloudcom.2013.91
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Barker
中科院分区：
文献类型：
--
作者：
Michael Luckeneder;A. Barker
在编排高度分布式和数据密集型的 Web 服务工作流时，编排引擎的地理位置可能会极大地影响工作流的整体性能。编排引擎通常在组织的网络内运行，并且可能必须跨越很长的地理距离传输数据，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，我们介绍了 Cloud Forecast：一个 Web 服务框架和分析工具，它给出工作流程规范，计算最佳 Amazon EC2 云区域以自动部署编排引擎并执行工作流程。我们使用工作流的地理距离、网络延迟以及 Amazon 云区域和工作流节点之间的 HTTP 往返时间来查找云区域的排名。这组简单指标的组合可以有效地预测工作流编排引擎应部署在何处，以减少总体执行时间。我们通过执行部署在 Planet Lab 平台上的随机生成的数据密集型工作流程来评估我们的方法，以便对 Amazon EC2 云区域进行排名。我们的实验结果表明，根据特定的工作流程，我们提出的优化策略与本地执行相比，可以将执行时间平均加快 82.25%。我们还表明，使用优化策略，执行时间的标准偏差平均减少了近 65%。
When orchestrating highly distributed and data-intensive Web service workflows the geographical placement of the orchestration engine can greatly affect the overall performance of a workflow. Orchestration engines are typically run from within an organisations' network, and may have to transfer data across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper we present Cloud Forecast: a Web service framework and analysis tool which given a workflow specification, computes the optimal Amazon EC2 Cloud region to automatically deploy the orchestration engine and execute the workflow. We use geographical distance of the workflow, network latency and HTTP round-trip time between Amazon Cloud regions and the workflow nodes to find a ranking of Cloud regions. This combined set of simple metrics effectively predicts where the workflow orchestration engine should be deployed in order to reduce overall execution time. We evaluate our approach by executing randomly generated data-intensive workflows deployed on the Planet Lab platform in order to rank Amazon EC2 Cloud regions. Our experimental results show that our proposed optimisation strategy, depending on the particular workflow, can speed up execution time on average by 82.25% compared to local execution. We also show that the standard deviation of execution time is reduced by an average of almost 65% using the optimisation strategy.