On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Computing resource prediction for mapreduce applications using decision tree
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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With the emergence of dynamic video processing, such as in image analysis, runtime estimation of resource usage would be highly attractive for automatic parallelization and QoS control with shared resources. A possible solution is to characterize the application execution using model descriptions of the resource usage. In this paper, we introduce Triple-C, a prediction model for Computation, Cache-memory and Communication-bandwidth usage with scenario-based Markov chains. As a typical application, we explore a medical imaging function to enhance objects of interest in X-ray angiography sequences. Experimental results show that our method can be successfully applied to describe the resource usage for dynamic image-processing tasks, even if the flow graph dynamically switches between groups of tasks. An average prediction accuracy of 97% is reached with sporadic excursions of the prediction error up to 20–30%. As a case study, we exploit the prediction results for semi-automatic parallelization. Results show that with Triple-C prediction, dynamic processing tasks can be executed in real-time with a constant low latency.