This paper studies the problem of online job scheduling in a model with preemption penalty introduced by Zheng et al. [11]. In such a model with preemption penalty parameter ρ, the scheduler has to pay a penalty of ρ times the weight of each aborted job. We consider two cases according to the scheduler's knowledge of Δ (ratio of length between longest and shortest jobs). In the first case where the exact value of Δ is known at the beginning, we re-investigate the WAL algorithm of Zheng et al. and prove that it is ((1 + ρ)Δ + o(Δ))-competitive for sufficiently large Δ. In particular, when ρ= 1, the previous competitive ratio of 3Δ + o(Δ) proved in [11] is improved to 2Δ + o(Δ). In the second case where the online strategy only knows beforehand that Δ ≥ k 3(ρ + 1)3 for some parameter k > 1, a (k(1+p)/k-1Δ+o(Δ))-competitive deterministic strategy is presented. For large Δ, the competitive ratio approaches that of WAL as k increases. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Zheng, F., Xu, Y., & Poon, C. K. (2009). On job scheduling with preemption penalties. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5564 LNCS, pp. 315–325). https://doi.org/10.1007/978-3-642-02158-9_27
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