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Service providers use these components to develop, deploy and execute applications on different clouds.
In the context of Future Internet [30], some frameworks have been proposed to deploy and execute adaptable, QoS-aware service compositions.
While theoretical concepts have been presented for balanced structures, much less is known about how organizations deploy and execute these solutions.
This includes all the actions needed to deploy and execute partitioned workflows over federated clouds: storing data, creating and executing workflows.
Applications require significant design effort and complex coordination of resources to define, deploy and execute components on heterogeneous and often unknown resources.
Moreover, we take advantage of the OMS architecture to deploy and execute data-centric workflows in a decentralized manner across multiple distinct Cloud resources, avoiding limitations of all data passing through a centralized engine.
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In this section we first discuss how the proposed algorithms can be deployed and executed in a real network.
It is a package of files generated by the service compiler, allowed to be deployed, and executed in real systems.
We have successfully deployed and executed the Montage astronomy workflow, often used as a benchmark, and we report on initial results of its performance evaluation.
The combination of service oriented architectures and business processes creates an enactment environment in which processes can be deployed and executed automatically.
In this work, we propose Kuaa, a workflow-based framework that can be used for designing, deploying, and executing machine learning experiments in an automated fashion.
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