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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.
According to SOA principles, a distributed software system can be seen as an orchestration of software services deployed and executed onto networked hosts.
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.
In particular, we built the algorithms using the component libraries and toolchain of the Lorien OS [27] deployed and executed on TelosB motes.
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While theoretical concepts have been presented for balanced structures, much less is known about how organizations deploy and execute these solutions.
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.
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.
However, the engineering methods and software tools used for developing, deploying and executing classical time critical applications do not, as yet, account for the programmability and controllability provided by clouds, and so time critical applications cannot yet benefit from the full potential of cloud technology.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com