Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuous-time model for capacity scaling, where the goal is to minimize the weighted sum of flow-time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called Adaptive Balanced Capacity Scaling (ABCS), that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that ABCS is $(1+\varepsilon)$-competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate. Finally, we investigate the performance of this algorithm on a real-world dataset and carry out extensive numerical experiments, which positively support the theoretical results.

翻译：现代数据中心受到巨大的电力消耗。 结果,数据中心操作员在能力规模解决方案上投入了大量资金,如果需求低,它们会动态地停止服务器使用,并在工作量增加时再次激活。我们分析一个连续时间的能力规模模型,目标是以在线方式最大限度地减少流动时间、转换成本和电力消耗的加权总和。我们提出了一个叫作适应性平衡能力增强(ABCS)的新算法,这个算法可以获取黑盒机器学习预测。ABCS旨在适应预测,同时也能抵御不可预测的工作量激增。特别是,我们证明ABCS如果预测准确,那么ABCS(1 ⁇ varepsilon)就具有1美元竞争力,然而,即使预测完全不准确,它也具有统一的约束性竞争比率。最后,我们调查这个算法在现实世界数据集上的运行情况,并进行广泛的数字实验,这很好地支持理论结果。