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In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines.
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It changes the mapping choices of randomly chosen tasks; that is, it associates these tasks to another processing unit.
The simulation results using randomly generated task graphs and task graphs corresponding to real world problems (GE and FFT) demonstrates that the proposed algorithm is able to deal with fluctuations of resource availability and provides overall optimal performance.
Our results show that when subjects are exposed to randomly varying tasks of the same structure, the motor control process can extract the structure of the task and thereby exhibit structure-specific facilitation, interference reduction, and exploration.
We will present results comparing gEDF with other real-time algorithms including, EDF, Best-effort, and Guarantee, by using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads.
Then the ITA is ready to automatically create rehabilitation sessions, by randomly assigning tasks from the different four suitability quartiles, until the maximum duration of the session is reached (by default, a rehabilitation session lasts one hour).
Each time when CLASSIFY is executed, it first selects randomly a task out of the unclassified task set Y ̂.
The order of the different task × TMS conditions varied randomly across task blocks.
The test environment is developed using randomly generated task graphs.
Extensive simulations are carried out under real-world benchmarks and randomly generated task graphs to demonstrate the effectiveness and efficiency of the proposed schemes.
This section presents the results of simulating our approach on a set of synthetic and randomly generated task sets.
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