  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
CloudForecast: 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 PlanetLab 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.
