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Second, minimize the scheduling delay.
The CEED problem aims to minimize the scheduling cost and greenhouse gases (GHGs) emission cost.
The main objective is to minimize the scheduling length of the task graph and maximize the lifetime of the sensor nodes.
In Fig. 11, the scheduling length resulted from our scheme is compared with the CNPT algorithm proposed in [18], whose only objective is to minimize the scheduling length for each energy budget.
The main objective for the first phase is to minimize the scheduling length (shown in the first part of the objective function) and also satisfy the precedence constraint (12).
The solution objectives are to minimize the scheduling length of the task graph and maximize the fairness in energy-driven task mapping, while satisfying energy harvesting causality constraints and the task precedence constraints.
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This is to minimize the schedule misses and to efficiently do the forwarding job with minimum delay.
Previous DAG scheduling methods tried to presuppose the minimum cost assignment for each task to minimize the schedule length of budget constrained applications on heterogeneous cloud computing systems.
In this section, the performance evaluation of our scheme in terms of our objectives which are minimizing the scheduling length and maximizing the energy-balancing considering energy harvesting characteristics are presented.
The objective function shows the trade-off between the two task allocation objectives with weights w 1 and w 2. The first part corresponds to minimizing the scheduling length by minimizing the maximum of the finish times of all the tasks.
In this study, we propose an efficient algorithm of minimizing the schedule length using the budget level (MSLBL) to select processors for satisfying the budget constraint and minimizing the schedule length of an application.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com