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.
@cite_5: This paper presents FICAS, a distributed data-flow infrastructure for composing software services into megaservices. We discuss the basic features of FICAS that enable the distribution of data-flows within megaservices. An autonomous service access protocol, ASAP, is defined to enforce the explicit separation of control-flows from dataflows of software services. We illustrate the procedure to construct and optimize megaservice execution plans that form distributed data-flows among collaborating services. The megaservice performance under FICAS is evaluated and compared with that under the centralized data-flow infrastructures. FICAS enhances the megaservice performance and is especially suitable for large-scale service composition.
The or FICAS @cite_5 is a distributed data-flow architecture for composing software services. Composition of the services in the FICAS architecture is specified using the Compositional Language for Autonomous Services (CLAS), which is essentially a sequential specification of the relationships among collaborating services. This CLAS program is then translated by the build-time environment into a control sequence that can be executed by the FICAS runtime environment.