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Book Sections Year : 2011

Parallel Evolutionary Algorithms for Energy Aware Scheduling


Reducing energy consumption is an increasingly important issue in computing and embedded systems. In computing systems, minimizing energy consumption can significantly reduces the amount of energy bills. The demand for computing systems steadily increases and the cost of energy continues to rise. In embedded systems, reducing the use of energy allows to extend the autonomy of these systems. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. This chapter gives an overview of the main methods used to reduce the energy consumption in computing and embedded systems. As a use case and to give an example of a method, the chapter describes our new parallel bi-objective hybrid genetic algorithm that takes into account the completion time and the energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms.
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inria-00637728 , version 1 (02-11-2011)


  • HAL Id : inria-00637728 , version 1


Yacine Kessaci, Mohand Mezmaz, Nouredine Melab, El-Ghazali Talbi, Daniel Tuyttens. Parallel Evolutionary Algorithms for Energy Aware Scheduling. Bouvry et al. (Eds.). Intelligent Decision Systems in Large-Scale Distributed Environments, 1st Edition., Springer Vlg., 2011, 2011, 978-3-642-21270-3. ⟨inria-00637728⟩
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