You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: This paper aims to address the problem of scheduling large workflows onto multiple execution sites with storage constraints. Three heuristics are proposed to first partition the workflow into sub-workflows. Three estimators and two schedulers are then used to schedule sub-workflows to the execution sites. Performance with three real-world workflows shows that this approach is able to satisfy storage constraints and improve the overall runtime by up to 48 over a default whole-workflow scheduling.
Workflow partitioning is an approach to divide a workflow into several sub-workflows, which are then executed on different sites. The most mature workflow partitioning mechanisms are contained in Pegasus [1] : a partitioner component decomposes an abstract workflow into smaller sub-workflows which are then mapped onto computational (usually Grid) resources.