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.

本文介绍了一种名为 Adaptive Balanced Capacity Scaling (ABCS) 的算法，该算法能够通过使用基于机器学习的预测来适应并减小数据中心的能耗和交换成本，同时保持足够的预测精度和竞争比率，相关的实验结果也证明了该算法的有效性。

网络容量扩展辅以不可靠机器学习预测

Online Capacity Scaling Augmented With Unreliable Machine Learning  Predictions

We consider the problem of minimizing a function represented as a sum of
submodular terms. We assume each term allows an efficient computation of {\em
exchange capacities}. This holds, for example, for terms depending on a small
number of variables, or for certain cardinality-dependent terms.
A naive application of submodular minimization algorithms would not exploit
the existence of specialized exchange capacity subroutines for individual
terms. To overcome this, we cast the problem as a {\em submodular flow} (SF)
problem in an auxiliary graph, and show that applying most existing SF
algorithms would rely only on these subroutines.
We then explore in more detail Iwata's capacity scaling approach for
submodular flows (Math. Programming, 76(2):299--308, 1997). In particular, we
show how to improve its complexity in the case when the function contains
cardinality-dependent terms.

本文研究了由多个子模块项组成的函数的最小化问题，其中每个项都可以有效地计算交换容量，将问题转化为辅助图中的子模块流问题，并探讨了 Iwata 的容量扩展方法在包含基数相关项时的复杂性改进。