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The goal of the allocation is to minimize the average percentage of energy consumed by the application to execute across the machines in the ad hoc grid, while meeting an application execution time constraint.
T is the total execution time constraint for a task.
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This paper considers the problem of finding the set of service providers that minimizes the total execution time of the business process subject to cost and execution time constraints.
For this reason, a Mixed Integer Programming (MIP) model is also proposed to determine the Ht-MPSoC configuration that consumes the least HW resources while respecting the application execution time constraints.
The longest path delays between operators constitute a basis for describing pipeline execution time constraints.
Parallel tasks are heavy in their nature, and therefore, a natural candidate for this model if execution time constraints is present.
Such a navigation problem requires a method that is able to yield a fast execution time, under constraints on the capacity of the robot and on known/unknown obstacles, and that is sufficiently flexible to deal with errors in the known parts of the environment (unexpected obstacles).
One of the most challenging problem is to optimize workflow execution time under QoS constraint of budget.
Thus, the design flow requires a floating-point to fixed-point conversion stage which optimizes the implementation cost under execution time and accuracy constraints.
The original dataset contained 250 variables, which is highly dimensional and is not practical due to limitations of execution time and memory constraints on a desktop computer.
Thus, the design flow requires a floating-to-fixed-point conversion stage which optimizes the implementation cost under execution time and accuracy constraints [1].
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