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More importantly, it paves the way for improving the data processing efficiency and avoiding data execution congestion.
They are: Planning, Test Case Design, Setup of Test Environment and Data, Execution and Evaluation, and Monitoring and Control.
A prototype has been implemented to carry out an exhaustive experimental evaluation through Master/Worker and Single Program Multiple Data execution models.
Many applications, in particular in image processing (e.g., an intelligent embedded camera), have dependent data execution times according to the nature of the input to be processed.
The system, built using Amazon Web Services (AWS), automates access and pre-processing of forecast data, execution of a computationally expensive high-resolution 2D hydrodynamic model, Two-dimensional Unsteady Flow (TUFLOW), and map-based visualization of model outputs.
The fundamental data execution was carried out using the Mann-Kendall test (Mann 1945; Kendall 1975) and auto-correlation function (ACF) analysis to verify the maximum 24 h consecutive rainfall which is convenient for regional frequency analysis.
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The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU.
The online nature is determined by the ability to access the performance data during execution and make it available to analysis and visualization tools, which are typically external.
The three types of state machine explained are: data indexed, execution indexed, and hybrid— the execution indexed is typically the most common.
Also, system performance for tasks with data dependent execution times should be improved by using dynamic schedulers instead of static (at compile time) scheduling techniques [11, 12].
In Explicit Data Graph Execution (EDGE) architectures [11], operations are statically assigned to execution units, but they are scheduled dynamically in a data-flow fashion.
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