Modern data centers suffer from immense power consumption. The erratic behavior of internet traffic forces data centers to maintain excess capacity in the form of idle servers in case the workload suddenly increases. As an idle server still consumes a significant fraction of the peak energy, data center operators have heavily invested in capacity scaling solutions. In simple terms, these aim to deactivate servers if the demand is low and to activate them again when the workload increases. To do so, an algorithm needs to strike a delicate balance between power consumption, flow-time, and switching costs. Over the last decade, the research community has developed competitive online algorithms with worst-case guarantees. In the presence of historic data patterns, prescription from Machine Learning (ML) predictions typically outperform such competitive algorithms. This, however, comes at the cost of sacrificing the robustness of performance, since unpredictable surges in the workload are not uncommon. The current work builds on the emerging paradigm of augmenting unreliable ML predictions with online algorithms to develop novel robust algorithms that enjoy the benefits of both worlds. 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 ML predictions, but is completely oblivious to the accuracy of these predictions. In particular, if the predictions turn out to be accurate in hindsight, we prove that ABCS is $(1+\varepsilon)$-competitive. Moreover, even when the predictions are inaccurate, ABCS guarantees a bounded competitive ratio. The performance of the ABCS algorithm on a real-world dataset positively support the theoretical results.

中文翻译：

---

不可靠的机器学习预测增强容量扩展的新方法

现代数据中心承受着巨大的功耗。互联网流量的不稳定行为迫使数据中心以闲置服务器的形式维持过剩的容量，以防工作负载突然增加。由于空闲服务器仍然消耗大部分的峰值能量，因此数据中心运营商已在容量扩展解决方案上投入了大量资金。简而言之，它们的目的是在需求低时停用服务器，并在工作负载增加时再次激活它们。为此，算法需要在功耗，流时间和切换成本之间达成微妙的平衡。在过去的十年中，研究团体开发了具有最坏情况保证的竞争性在线算法。在存在历史数据模式的情况下，来自机器学习（ML）预测的处方通常胜过此类竞争算法。但是，这是以牺牲性能的健壮性为代价的，因为工作负载的不可预测的激增并不罕见。当前的工作建立在新兴的范式上，该范式通过在线算法来增强不可靠的ML预测，从而开发出新颖的，健壮的算法，该算法既可以受益于这两个世界。我们分析了用于容量扩展的连续时间模型，该模型的目标是以在线方式最大程度地减少流量时间，切换成本和功耗的加权总和。我们提出了一种称为自适应平衡容量缩放（ABCS）的新颖算法，该算法可以访问黑盒ML预测，但完全不考虑这些预测的准确性。特别是，如果事后预测这些预测是准确的，则我们证明ABCS具有$（1+ \ varepsilon）$竞争性。此外，即使预测不准确，ABCS也会保证有限的竞争比率。ABCS算法在真实数据集上的性能肯定支持理论结果。