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In this paper, we study the problem of task scheduling in energy harvesting embedded systems.
We ponder the problem of task scheduling on a multi-core processor with software controlled DVS where the objective is to reduce the energy consumption.
Next, this paper makes use of SFLA trained Artificial Neural Network (ANN) and Radial Basis function Neural Networks (RBFNN) for the problem of task scheduling.
In this paper we present several solutions to the problem of task allocation among autonomous agents, and suggest that the agents form coalitions in order to perform tasks or improve the efficiency of their performance.
Specifically, we define the problem of task allocation in which the private information of the agents is not only their costs of attempting the tasks but also their probabilities of failure.
In addition, in order to satisfy the utility acquirement and the energy budget, Wu et al. [19] proposed a unimodal arbitrary arrival with energy bounds algorithm (EBUA), and the EBUA solved the problem of task scheduling based on unimodal arbitrary arrival model.
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Most existing studies on spatial crowdsourcing mainly focus on the problems of task assignment [6, 7, 8, 9, 10, 11, 12], which are to assign tasks to suitable workers, and assume that tasks are all simple and trivial.
In this paper, we study the problem of multi-task job scheduling at a data center with the goal of maximizing the total utility of all the jobs, where each job is decomposed into multiple tasks, and both a job and a task have their own utility.
This study aims to investigate the problem of emergency task planning for agile satellites.
In this paper, we only address the problem of map task assignment.
Hungarian algorithm was put forward by Hungarian mathematician Edmonds in 1965, and this algorithm is generally used to solve the problem of linear task allocation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com