MapReduce has emerged as a prominent programming model for data-intensive computation. In this work, we study power-aware MapReduce scheduling in the speed scaling setting first introduced by Yao et al. [FOCS 1995]. We focus on the minimization of the total weighted completion time of a set of MapReduce jobs under a given budget of energy. Using a linear programming relaxation of our problem, we derive a polynomial time constant-factor approximation algorithm. We also propose a convex programming formulation that we combine with standard list scheduling policies, and we evaluate their performance using simulations. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Bampis, E., Chau, V., Letsios, D., Lucarelli, G., Milis, I., & Zois, G. (2014). Energy efficient scheduling of mapreduce jobs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8632 LNCS, pp. 198–209). Springer Verlag. https://doi.org/10.1007/978-3-319-09873-9_17
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