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]: 

[2]: In today's large collaborative environments, potentially composed of multiple distinct organisations, uniform controlled access to data has become a key requirement if these organisations are to work together as Virtual Organisations. We refer to such an integrated set of data resources as a virtual data warehouse. The Open Grid Services Architecture – Data Access and Integration (OGSA-DAI) project was established to produce a common middleware solution, aligned with the Global Grid Forum's (GGF) OGSA vision [OGSA] to allow uniform access to data resources using a service based architecture. In this paper the service infrastructure provided by OGSA-DAI is presented providing a snapshot of its current state, in an evolutionary process, which is attempting to build infrastructure to allow easy integration and access to distributed data using grids or web services. More information about OGSA-DAI is available from the project web site: www.ogsadai.org.

[3]: We discuss here a parallel implementation of the visualisation of data from a galaxy formation simulation within the Triana problem-solving environment. The visualisation is a test case for our prototype Triana Grid software using a decentralized peer-to-peer approach. The Triana distributed environment is middleware independent and designed in such a way as to make it easily extensible by users of the system. This novel approach is usable both within a peer-to-peer framework, using JXTA, and within service oriented architectures, such as OGSA, by the use of a Grid application toolkit (GAT) interface. Furthermore, Triana has a pluggable architecture that allows the middleware independent representations of distributed task-graphs to be written in several popular formats. This facilitates the use of the mechanism within other disparate systems. Galaxy-formation simulation codes generate data that represent a series of particles in three dimensions and their associate properties as snapshots in time. An important aspect of these types of calculation is the visualization of time sequence data. The user of the code would like to visualize this data as an animation in two dimensions with the ability to vary the point of view, project that particular two dimensional slice and re-run the animation. The recalculation of the projection can be computed in parallel resulting in a considerable speedup.
[1] submits jobs represented as a DAG to a Condor pool of resources. Intermediate data are not transferred via a workflow engine, instead they are passed directly from vertex to vertex. DAGMan removes the workflow bottleneck as data are transferred directly between vertices in a the DAG. [3] is an open-source problem solving environment. It is designed to define, process, analyse, manage, execute and monitor workflows. Triana can distribute sections of a workflow to remote machines through a connected peer-to-peer network. [2] is a middleware product that supports the exposure of data resources on to Grids. This middleware facilitates data streaming between local OGSA-DAI instances.