On Job Scheduling with Preemption Penalties

  • Authors:

  • Feifeng Zheng;Yinfeng Xu;Chung Keung Poon

  • Affiliations:

  • School of Management, Xi'an JiaoTong University, Xi'an, China 710049 and The State Key Lab for Manufacturing Systems Engineering, Xi'an, China 710049;School of Management, Xi'an JiaoTong University, Xi'an, China 710049 and The State Key Lab for Manufacturing Systems Engineering, Xi'an, China 710049;City University of Hong Kong, Hong Kong, China

  • Venue:
  • AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 $(\frac{k(1+\rho)}{k-1}\Delta+o(\Delta))$-competitive deterministic strategy is presented. For large Δ , the competitive ratio approaches that of WAL as k increases.