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On Packet Scheduling with Adversarial Jamming and Speedup

机译:在对抗对抗的数据包安排和加速

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In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes arrive over time to be transmitted over a channel in which instantaneous jamming errors occur at times chosen by the adversary and not known to the algorithm. The transmission taking place at the time of jamming is corrupt, and the algorithm learns this fact immediately. An online algorithm maximizes the total size of packets it successfully transmits and the goal is to develop an algorithm with the lowest possible asymptotic competitive ratio, where the additive constant may depend on packet sizes. Our main contribution is a universal algorithm that works for any speedup and packet sizes and, unlike previous algorithms for the problem, it does not need to know these properties in advance. We show that this algorithm guarantees 1-competitiveness with speedup 4, making it the first known algorithm to maintain 1-competitiveness with a moderate speedup in the general setting of arbitrary packet sizes. We also prove a lower bound of Φ + 1 ≈ 2.618 on the speedup of any 1-competitive deterministic algorithm, showing that our algorithm is close to the optimum. Additionally, we formulate a general framework for analyzing our algorithm locally and use it to show upper bounds on its competitive ratio for speedups in [1,4) and for several special cases, recovering some previously known results, each of which had a dedicated proof. In particular, our algorithm is 3-competitive without speedup, matching the algorithm and the lower bound of Jurdzinski et al. [7]. We use this framework also for the case of divisible packet sizes in which the size of a packet divides the size of any larger packet, to show that a slight modification of our algorithm is 1-competitive with speedup 2 and it achieves the optimal competitive ratio of 2 without speedup, again matching the algorithm and the lower bound of [7].
机译:在随着对惯性干扰误差发生的频道上,在竞争中发生瞬时干扰误差发生的频道上,随着前尺寸的频道尺寸的分组调度,随着时间的推移,在瞬时发生的瞬时发生干扰误差而不知道。在干扰时发生的传输损坏,并且算法立即了解这一事实。在线算法最大化其成功传输的数据包的总大小,并且目标是开发一种具有尽可能低的渐近竞争比率的算法,附加常数可能取决于分组尺寸。我们的主要贡献是一种通用算法,适用于任何加速和数据包大小,与此问题的算法不同,它不需要提前了解这些属性。我们展示该算法保证了具有加速4的1竞争力,使其成为维持1竞争力的第一次算法,在任意数据包大小的常规设置中具有中等的加速。我们还在任何1个竞争确定性算法的加速度上证明φ+1≈.2618的下限,表明我们的算法接近最佳。此外,我们制定了一般框架,用于本地分析我们的算法,并使用它在[1,4)中的加速比其竞争性比例显示上限,以及几种特殊情况,恢复了一些先前已知的结果,每个特殊情况都有一个专用证明。特别是,我们的算法是3竞争而无需加速,匹配算法和Jurdzinski等人的下限。 [7]。我们也使用此框架对于可分定数的数据包大小的情况,其中数据包的大小划分了任何较大数据包的大小,以表明我们的算法的略微修改是具有加速2的1竞争力,并且它实现了最佳的竞争比率2没有加速,再次匹配算法和下限[7]。

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