Abstract
We consider the problem of scheduling a set of jobs, each one specified by its release date, its deadline and its processing volume, on a set of heterogeneous speed-scalable processors, where the energy-consumption rate is processor-dependent. Our objective is to minimize the total energy consumption when both the preemption and the migration of jobs are allowed. We propose a new algorithm based on a compact linear programming formulation. Our method approaches the value of the optimal solution within any desired accuracy for a large set of continuous power functions. Furthermore, we develop a faster combinatorial algorithm based on flows for standard power functions and jobs whose density is lower bounded by a small constant. Finally, we extend and analyze the AVerage Rate (AVR) online algorithm in the heterogeneous setting.
S. Albers—Work supported by the German Research Foundation, projects Al 464/ 7-1 and Al 464/9-1.
E. Bampis—Research partially supported by projet GDR-RO AGaPe of CNRS.
D. Letsios—Research partially supported by ANR project Stint and ANR program “Investments for the Future”.
G. Lucarelli—Research supported by projet ANR Moebus.
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Albers, S., Bampis, E., Letsios, D., Lucarelli, G., Stotz, R. (2016). Scheduling on Power-Heterogeneous Processors. In: Kranakis, E., Navarro, G., Chávez, E. (eds) LATIN 2016: Theoretical Informatics. LATIN 2016. Lecture Notes in Computer Science(), vol 9644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49529-2_4
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DOI: https://doi.org/10.1007/978-3-662-49529-2_4
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