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Greedy Minimum-Energy Scheduling (2307.00949v1)

Published 3 Jul 2023 in cs.DS

Abstract: We consider the problem of energy-efficient scheduling across multiple processors with a power-down mechanism. In this setting a set of $n$ jobs with individual release times, deadlines, and processing volumes must be scheduled across $m$ parallel processors while minimizing the consumed energy. Idle processors can be turned off to save energy, while turning them on requires a fixed amount of energy. For the special case of a single processor, the greedy Left-to-Right algorithm guarantees an approximation factor of $2$. We generalize this simple greedy policy to the case of $m \geq 1$ processors running in parallel and show that the energy costs are still bounded by $2 \text{OPT} + P$, where $\text{OPT}$ is the energy consumed by an optimal solution and $P < \text{OPT}$ is the total processing volume. Our algorithm has a running time of $\mathcal{O}(n f \log d)$, where $d$ is the difference between the latest deadline and the earliest release time, and $f$ is the running time of a maximum flow calculation in a network of $\mathcal{O}(n)$ nodes.

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References (8)
  1. Parallel machine scheduling to minimize energy consumption. In Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’20, page 2758–2769, USA, 2020. Society for Industrial and Applied Mathematics.
  2. Skeletons and Minimum Energy Scheduling. In H.-K. Ahn and K. Sadakane, editors, 32nd International Symposium on Algorithms and Computation (ISAAC 2021), volume 212 of Leibniz International Proceedings in Informatics (LIPIcs), pages 51:1–51:16, Dagstuhl, Germany, 2021. Schloss Dagstuhl – Leibniz-Zentrum für Informatik. ISBN 978-3-95977-214-3. doi: 10.4230/LIPIcs.ISAAC.2021.51. URL https://drops.dagstuhl.de/opus/volltexte/2021/15484.
  3. P. Baptiste. Scheduling unit tasks to minimize the number of idle periods: A polynomial time algorithm for offline dynamic power management. In Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm, SODA ’06, page 364–367, USA, 2006. Society for Industrial and Applied Mathematics. ISBN 0898716055.
  4. Polynomial time algorithms for minimum energy scheduling. In European Symposium on Algorithms, pages 136–150. Springer, 2007.
  5. P. Brucker. Scheduling Algorithms, volume 47. 01 2004. doi: 10.2307/3010416.
  6. Scheduling to minimize gaps and power consumption. In Proceedings of the Nineteenth Annual ACM Symposium on Parallel Algorithms and Architectures, SPAA ’07, page 46–54, New York, NY, USA, 2007. Association for Computing Machinery. ISBN 9781595936677. doi: 10.1145/1248377.1248385.
  7. Optimization and approximation in deterministic sequencing and scheduling : a survey. Annals of Discrete Mathematics, 5:287–326, 1979. ISSN 0167-5060. doi: 10.1016/S0167-5060(08)70356-X.
  8. Algorithms for power savings. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, January 12-14, 2003, Baltimore, Maryland, USA, pages 37–46. ACM/SIAM, 2003. URL http://dl.acm.org/citation.cfm?id=644108.644115.

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