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Dynamic speed scaling to manage energy and temperature

机译:动态速度缩放以管理能量和温度

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We first consider online speed scaling algorithms to minimize the energy used subject to the constraint that every job finishes by its deadline. We assume that the power required to run at speed s is P(s) = s/sup /spl alpha//. We provide a tight /spl alpha//sup /spl alpha// bound on the competitive ratio of the previously proposed optimal available algorithm. This improves the best known competitive ratio by a factor of 2/sup /spl alpha//. We then introduce an online algorithm, and show that this algorithm's competitive ratio is at most 2(/spl alpha//(/spl alpha/ - 1))/sup /spl alpha//e/sup /spl alpha//. This competitive ratio is significantly better and is approximately 2e/sup /spl alpha/+1/ for large /spl alpha/. Our result is essentially tight for large /spl alpha/. In particular, as /spl alpha/ approaches infinity, we show that any algorithm must have competitive ratio e/sup /spl alpha// (up to lower order terms). We then turn to the problem of dynamic speed scaling to minimize the maximum temperature that the device ever reaches, again subject to the constraint that all jobs finish by their deadlines. We assume that the device cools according to Fourier's law. We show how to solve this problem in polynomial time, within any error bound, using the ellipsoid algorithm.
机译:我们首先考虑在线速度缩放算法,以最大程度地减少能耗,这取决于每个工作在截止日期之前完成的约束。我们假设以速度s运行所需的功率为P(s)= s / sup / spl alpha //。我们提供了严格的/ spl alpha // sup / spl alpha //来约束先前提出的最佳可用算法的竞争比。这样可以将最知名的竞争比率提高2 / sup / spl alpha //。然后,我们介绍一种在线算法,并证明该算法的竞争率最多为2(/ spl alpha //(/// spl alpha /-1))/ sup / spl alpha // e / sup / spl alpha //。对于大的/ spl alpha /,此竞争比率要好得多,大约为2e / sup / spl alpha / + 1 /。对于大的/ spl alpha /,我们的结果基本上是紧密的。特别是,当/ spl alpha /逼近无穷大时,我们证明了任何算法都必须具有竞争比e / sup / spl alpha //(不超过低阶项)。然后,我们转向动态速度缩放的问题,以最大程度地降低设备曾经达到的最高温度,这又受到所有作业必须按时完成的约束。我们假设设备根据傅立叶定律进行冷却。我们展示了如何使用椭球算法在任意误差范围内的多项式时间内解决此问题。

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