Abstract
Reducing energy consumption is an increasingly important issue in computing and embedded systems. In computing systems, minimizing energy consumption can significantly reduce 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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Kessaci, Y., Mezmaz, M., Melab, N., Talbi, EG., Tuyttens, D. (2011). Parallel Evolutionary Algorithms for Energy Aware Scheduling. In: Bouvry, P., González-Vélez, H., Kołodziej, J. (eds) Intelligent Decision Systems in Large-Scale Distributed Environments. Studies in Computational Intelligence, vol 362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21271-0_4
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DOI: https://doi.org/10.1007/978-3-642-21271-0_4
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