An Iterative Optimization Framework for Adaptive Workflow Management in Computational Clouds

As more and more data can be generated at a faster-than-ever rate nowadays, it becomes a challenge to processing large volumes of data for complex data analysis. In order to address performance and cost issues of big data processing on clouds, the authors present a novel design of adaptive workflow management system which includes an SVM (Support Vector Machine) based prediction model, workflow scheduler, and iteration controls to optimize the data processing via iterative workflow tasks. They proposed a new heuristic algorithm, called upgrade fit, which dynamically and continuously reallocates multiple types of cloud resources to fulfill the performance and cost requirements.

Provided by: University of Medicine and Pharmacy Topic: Cloud Date Added: Feb 2013 Format: PDF

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